Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning
Authors: Hao Tan
jun lan
Zichang Tan
Senyuan Shi
Ajian Liu
Chuanbiao Song
Huijia Zhu
Weiqiang Wang
Jun Wan
Zhen Lei
Venue: ICLR 2026 Oral (2026)
Keywords: Deepfake Detection
MLLMs
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce a MLLM-based detector for transparent deepfake detection, along with a holistic dataset for deepfake detection.

Abstract: Deepfake detection remains a formidable challenge due to the evolving nature of fake content in real-world scenarios. However, existing benchmarks suffer from severe discrepancies from industrial practice, typically featuring homogeneous training sources and low-quality testing images, which hinder the practical usage of current detectors. To mitigate this gap, we introduce **HydraFake**, a dataset that contains diversified deepfake techniques and in-the-wild forgeries, along with rigorous training and evaluation protocol, covering unseen model architectures, emerging forgery techniques and novel data domains. Building on this resource, we propose **Veritas**, a multi-modal large language model (MLLM) based deepfake detector. Different from vanilla chain-of-thought (CoT), we introduce *pattern-aware reasoning* that involves critical patterns such as "planning" and "self-reflection" to emulate human forensic process. We further propose a two-stage training pipeline to seamlessly internalize such deepfake reasoning capacities into current MLLMs. Experiments on HydraFake dataset reveal that although previous detectors show great generalization on cross-model scenarios, they fall short on unseen forgeries and data domains. Our Veritas achieves significant gains across different out-of-domain (OOD) scenarios, and is capable of delivering transparent and faithful detection outputs.

Published as a conference paper at ICLR 2026
VERITAS: GENERALIZABLE DEEPFAKE DETECTION VIA PATTERN-AWARE REASONING
Hao Tan 12,33 Jun Lan
Zichang Tan Senyuan Shi
Ajian Liu
Chuanbiao Song²
Huijia Zhu Weiqiang Wang² Jun Wan 1.2,5g Zhen Lei
School of Advanced Interdisciplinary Sciences (SAIS), University of Chinese Academy of Sciences
"MAIS, Institute of Automation, Chinese Academy of Sciences *Ant Group
"Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences
Schooo of Artificial Intelligence, University of Chinese Academy of Sciences (tanhao2023, jun wan, zhen. lei e ac.cn ye welannnnn.... gou.tggtnnuuuuuuoo com
Prroject Lead. $Corresponding author.
? Project Page: httpss//gthbb.ccm////////////ttt
ABSTRACT
Deepfake detection remains a formidable challenge due to the evolving nature of fake content in real-world scenarios. However, existing benchmarks suffer from severe discrepancies from industrial practice, typically featuring homoge- neous training sources and low-quality testing images, which hinder the practi- cal usage of current detectors. To mitigate this gap, we introduce Hydra dataset that contains diversified deepfake techniques and in-the-wild forgeries, along with rigorous training and evaluation protocol, covering unseen model ar- chitectures, emerging forgery techniques and novel data domains. Building on this resource, we propose VERITAS, multi-modal large language model (MLLM) based deepfake detector. Different from vanilla chain-of of-thought (CoT), we in- troduce pattem--aaae reasoning that involves critical patterns such "planning" and "self-reflection" to emulate human forensic process. We further propose a two-stage training pipeline to seamlessly internalize such deepfake reasoning ca- pacities into current MILMs. Experiments on HydraFake dataset reveal that al- though previous detectors show great generalization on cross-model scenarios, they fall short on unseen forgeries and data domains, Our VERITAS achieves sig- nificant gains across different out-of-domain (OOD) scenarios, and is capable of delivering transparent and faithful detection outputs.
INTRODUCTION
Recent advances in Generative AI (Esser et al. 2024 Tian et al. 2024) have revolutionized our digital life, unprecedentedly enriching the diversity of content on social media and short-video platforms. Though bringing immense creativity, such techniques also enable highly convincing deepfakes with minimal cost, posing significant security risks to society. Consequently, Deepfake Detection (DFD), which aims discerning between real and generated facial images, has become heated research frontier, galvanizing extensive efforts.
However, current detectors mostly follow standard evaluation, which involves training on one dataset (Rossler et al. 2019) and testing on others (Dolhansky et al. 2019 Li et al. 2020b; Dol- hansky al.] 2020: Zi et al. 2020 Zhou et al. 2021). Despite its popularity, this protocol fails to align with practical industrial scenarios, where abundant training samples are available yet signifi- cant out-o--distr (OOD) generalization challenges (e.g., brand-new forgery types and metic- ulously synthesized facial images) emerge during testing. Such diserepancy severely hinders the practical deployment of current detectors. To mitigate the gap, we construct HydraFake dataset. As shown in Figure 2 we systematically collect and reproduce advanced deepfake methods, covering diversified deepfake techniques and in-the-wild forgeries from social media. To simulate potential challenges in real-world scenarios, establish rigorous and holistic evaluation protocol, where the training set consists of abundant samples but is restricted to three basic forgery types, and the
"This work done during the first author's ''s intersship at Ant Group.

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Published as a conference paper at ICLR 2026
Please determine the out buthenticity of the image
GPT-44
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ieffecciior reflect lock
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Veritas (ours):
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splanning Given the iniaal impression of realimm, a.thoruggh and systematic cnninaation necessary. </plomnnng mreasonings ... The background is solid blue, which is common in profess and settings but can sometines be sigt a cantrelled or generated environment. drressonigg
creflections Wat a moment Thhre's anothur uspect to cansider. The bodee on the left side the suit disploys text, difficult 16 read clearly In real mmyes auch text andd usiily legible lesst discersible espacially when the subject facing comera ambiuity could delbbrrte attempt abscure or nislood, Al-generated imges
Fake Image from unseen domain, generated using Dreamina opps
serutiry of cconclusion... gotentid abfuscation Suggest generated, s/cancluiion <answer> fake c/onswer
Comparison of the detection outputs. Intern VL3-78B Zhu et al., 2025 gets incorrect answer. GPT-4o (Hurst et al. (2024) and our model trained without the proposed MiPO both fail to provide precise explanation. In contrast, our model gives transparent and faithful decision process.
Figure 1:
evaluation involves hierarchical OOD testing, spanning in-domain, cross-model, cross-forgery and cross-domain scenarios, enabling fine-graie fine-grained understanding of the model's 's capacities. As presented in Figure 2 (d), under such rigorous evaluation, current SOTA detectors show great generalization on cross- cross-model deepfakes, but limited abilities in cross-forgery and cross-domain scenarios.
To improve the robustness on unseen forgeries and data domains, we seek ground the general- ization abilities of multi-modal large language models (MLLMs) into deepfake detection. Recent efforts (Huang al. 2024: Guo al., 20256 Peng et al.) 2025) have made initial attempts, while they focus on the explainability and the classification is still based on expert vision models. In con- strast, we explore to seamlessly internalize MLLMs into deepfake detection through their intrinsic reasoning abilities. However, directly applying deep reasoning faces eritical challenge: current MLLMs are extremely short for deepfake detection (Ren et al.) 2025 Tariq et al.) (025).. Effective reasoning data is necessary ground the abilities of base model. To achieve this goal, must answer two key questions: (1) what kind of reasoning process is helpful to DFD task? and (2) with sufficient data, how
ensure the model is learning to reason for DFD rather than memorizing?
For the first question, we introduce a pattern-aware reasoning framework. Drawing inspiration from recent studies (Zhao al. 2025| Muennighoff' et al. 2025) that demonstrate critical reasoning patterns greatly elevate the OOD performance of LLMs, we consider the human mindset for deep- fake detection: when determining the authenticity of an image, we tend to make quick judgment based on our first impression (fast judgement), then identify one or two prominent features (reason- ing) to draw a conclusion (conclusion). For more challenging samples, we may conduct a layered analysis (planning), and may also engage in more in-depth thinking to support or overturn our initial judgement (self-reflection). Based on this analogy, we extract these five thinking patterns to fa- cilitate logical and holistic reasoning. Table empirccallr shows the benefits of such pattern-aware reasoning over vanilla training pipeline consisting of pattern-guided cold-start and pattern-awar exploration, yielding our PERITAS[mode During cold-start, we employ SFT to internalize thinking patterns. Besides, we introduce Mixed Preference Optimization (MiPO) strategy that leverages mixed non-preference data and human-annotated preference data to steer the model toward faithful and fine-grained rea- soning. As shown in Figure I MiPO greatly improves the reasoning quality, mitigating the memo- rizing behavior. To further facilitate adaptive planning and self-reflection, we propose Pattern-aw aware Group Relative Policy Optimization (P-GRPO), which shapes reasoning behavior through online sampling and pattern-awe aware reward mechanism. As a result, VERITAS shows great generalization on unseen forgeries and data domains, providing transparent and precise decision process (Figure I
(CoT). For the second question, we introduce a two-stage
To sum up. our main contributions are:
VERITAS means "Truth" in Latin.
2

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Published as a conference paper at ICLR 2026
Dataset: We introduce HydraFake, dataset that simulates real-world challenges with hierarchical generalization testing, advancing the evaluation protocol in deepfake detection and helping developers better locate the deficiencies of their detectors.
We propose a two-stage training pipeline that grounds the capabilities of MLLMs into deepfake detection through pattern-aware reasoning. Our model supports adaptive planning and self- reffecefle,,nn, delivering transparent and human- decision mndto-eed.
Method:
Performance: Our VERITAS model achieves significant improvements over state-of-the- art detectors cross-forgery and cross-domain scenarios, and cold-start model serves as a strong reasoning foundation for further customization.
2 RELATED WORK
2.1 DEEPFAKE DETECTION AND DATASETS
Deepfake detection aims distinguish generated facial images from authentic human faces. Previ- ous efforts have explored spatial-level (Ojha et al. 2023 Yan et al. [222bb] Tan et al. [2024b] Nguyen
al.
2024 Fu al. 2025 Yan al. 2024a Yang al.) 2025c). frequency- level (Qian al. Tan et al. 2024a Zhou et al. 2024: Kashiani et al. (025) and sequence-level (Gu et al. 2021
2020
2022b Yan et al. 2025) approaches, achieving remarkable progress on traditional benchmarks. To train a generalizable detector, some methods attempt to find "bias-free fake images either through
spatial-domain blending (Li et. al. 2020a Shiohara & Yamasaki 2022 Zhao et al. (221]) frequency- domain blending (Zhou et al. 2024 Kashiani et al.
2025)) or feature-leve augmentation Yan al.
2024b). However, the commonly adopted protocol, i.e., training on FF++ (Rossler et al. 2019) and
testing others (Dolhansky et al.
2019 Li al. 2020b Dolhansky al., 20,20 [Zi et al. 2020
Zhou et al. 2021), suffers from two problems: (1) the training sources are overly narrow, and (2) the
testing data exhibit limited forgery types and low-resolution. Although many timely datasets (Yan)
et al. 2024a /Zhang et al. 2024b] Li et al. 2025 Huang et al. 20255) Wang et al. 2025a Wen et al.,
2025a Xia al. 2025) have been proposed for AIGC detection, the pace of deepfake detection has lagged behind. As a result, previous methods are biased towards such settings, exhibiting degraded
generalization when learning from varying sources or mixed artifacts. To mitigate this problem, we introduce hierarchical protocol in our HydraFake dataset, aiming to comprehensively reflect the generalization capability of the detectors.
2.2 MILMS FOR DEEPFAKE DETECTION
With the proliferation of MLLMs (Luu et al. 2023 Bai et al. 2025 Zhu et al. 2025). recent fo- cus has shifted to explainable deepfake and AIGC detection. However, most methods still rely on small vision models for the final decision. For instance, M2F2-Det (Guo et al. 2025b) determines the authenticity purely based CLIP models, where LLM is leveraged Similarly, DD-VQA (Zhang et al. 2024a). FFAA (Huang et al. 2024) and VLF-FFD (Peng et al.) 2025) develop post-processing system aggregate embeddings from small vision models. Some methods (He et al. 2025) Sun et al. 2025 Chen et al. (0255) attempt to directly adopt the outputs from LLMs, e.g., Sun et al. (Sun al., (025)) construct precise forgery explanations
a plug-in interpreter.
release the power of MLLMs. Recent methods (Huang et al. 2025a Xu et al., 2024b; Zhou et al., 2025) also adopt MILMss and curated datasets for AIGC detection. However, these methods generate post-hoc explanations by first determining the answer. The potential of reasoning abilities for deepfake detec- tion still underexplored. The most recent methods (Gao et al. 2025 Xia et al. (025) explore the reasoning for AIGC detection, while neglecting adaptive reasoning patterns and is not tailored for facial forgery. Different from previous methods, we introduce human-like reasoning into deepfake detection, achieving promising improvements and delivering transparent decisions end-to-end.
HYDRAFAKE DATASET
In this part, we introduce our HydraFake dataset, including the construction process and evaluation protocol. Detailed statistics and information are provided in Appendix A.
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Data Collection
Evaluation Protocol
Hydrof PadroFokke-ooe 100K
Training Set (454)
PRA
Fake: molel artiftcts (FS. FR. EFG)
Reel LR+HR (5 adbett, ID leave eut)
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HR High Recolation (-302 -0224)0004) 10: Idennity
PS: Face Seagping
FR: Ficce Resnactment
EPG Bstire Pace Generation
1009 10WW0N) DOPO7 OGE (CWMPR6)
DOA
Figure 2: Overview of HydraFake dataset. (a) We carefully collect and reimplement advanced deepfake techniques to construct our HydraFake dataset. Real images are collected from 8 datasets. Fake images are from classic datasets, high-quality public datasets and our self-constructed deepfake data. (b) We introduce a rigorous evaluation protocol. Training data contains abundant samples but limited forgery types. Evaluations are split into four levels. (c) Illustration of the subsets in different evaluation splits. (d) Performance of existing detectors. Most detectors generalize well on Cross- Model setting but perform poorly on Cross-Forgery and Cross-Domain scenarios.
3.1 DATA COLLECTION
Real Images. As shown in Figure 2 (a). the real images are collected from 8 public datasets, con- taining both low-resolution (i.e., LFW (Huang et al., 2008), CelebA (Liu al. 2015), FaceForen- sics++ (FF++) (Rossler et al. 2019), FFIW (Dolhansky et al., 2019)) and high-resolution images
(i.e., FFHQ (Karras et al. 2019). VFHQ (Xie et al. 2022). UADFV (Yang et al. (209)) and Cele-
bAHQ (Karras et al. 2017) The collected images rigorously partitioned for training and testing.
Fake Images. The fake images come from three sources:
Classic deepfake data sampled from FF++ (Rossler al., 2019) DF40 Yan al. 2024d) and FFIW (Dolhansky et al. 2019), which mainly contain face swapping (FS) and face reenactment (FR) forgeries from 10 generative models. The artifacts are mostly localized.
Public deepfake data sampled from WILD (Bongini et al. 2025). seeprettyface website and Talk- ingHeadBench (Xiong et al., 2025). This contains carefully synthesized faces from 16 popular generators. However, there still exist corner cases such fresh forgery types.
Advanced deepfake data where we further reimplemented and crawled 10K deepfake data from 10 advanced generators. Besides traditional deepfake techniques. HydraFake dataset contains Face Restoration (Zhou et al.) 2022). Face Relighting (Zhang et al. (025b)), Face Personaliza- tion (Jiang et al., 2025; Guo et al., 2024), Generative Face Swapping (Han et al.) deepfakes from Visual AutoRegressive models (VAR) (Han et al.) (2025] Tang et al. 2024). To
2024) and
simulate real-world challenges, we also crawled 1K deepfake images from social media, which include practical deepfakes generated from commercial apps, including GPT-4o (Hurst et al. 2024), Dreamina (team] 2025a) and Hailuo Al (team) 2025b).
Quality Control. For classic deepfake datasets, we only select FF++ (Rossler et al. 2019 and FFIW (Zhou et al.] 2021), while not involving DFDC (Dolhansky et al. 2020). DFDCP (Dolhan- sky et al. 2019) and WDF (Zi et al. 2020) due to their low quality (e.g., unexpected blurring in real images). For our celf-constructed deepfake data, we conduct strict quality control, e.g., for face personalization, we use Qwen2.5 VL-72B to tailor sample-spent specific prompts rather than using template-like prompts as in (Bongini et al.) 2025). For face relighting, we generate multiple lighting sources for each identity and manually select high-quality samples. After filtering and balancing, our HydraFake datasct contains 50K real images and 50K fake images.
3.2 EVALUATION PROTOCOL
Training, As shown in Figure (b). the training set contains 48K images. Real images are from subsets, with other 3 subsets left out for testing. Fake images involves 21 subsets while only contains
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3 forgery types (i.e., FS, FR and EFG). This is to simulate practical setting, where abundant training images are available but various forgery types and generative models remain unseen.
Evaluation. The evaluation is divided into four distinct levels:
In-Domain (14K): testing images share the training data source but with different identities.
Cross-Model (11K): fake images are generated by unseen models under controlled conditions
like the template-based textual prompts. This includes SOTA models from recent years (c.g.,
FLUX1.1-Pro (Black Forest Labs) 2024), Adobe FireFly (Adobe, (023). Starry AI (Al 2023 ).
distinct model architectures (e.g., VAR (Han al., 2025] Tang et al., 2024) and Video AR
model (Sand-AI 2025)). The real images are from in domain set but with different identities.
Cross-Forgery (12K): fake images are generated by unseen manipulation techniques, involving attribute editing, generative face swapping. (P-preserved personalization, face relighting and face restoration. The real images are from in-domain set but with different identities. This split is to evaluate the model's capacity to detect fake images generated by unseen manipulation.
Cross-Domaite (15K); fake images are either generated under controlled conditions collected from the web, including both unseen forgeries and unseen models. The real images are from unseen datasets (i.e., VFHQ (Xie et al. 2022), UADFV YYang et al., (2019) and FFIW (Dolhansky et 2019)). The images are of different qualities, posing strong challenges.
4 METHOD
In this section, detail the two-stage training pipeline of VERITAS, including nattern-guided cold- start and pattern-aware reinforcement learning, as shown Figure
4.1 PATTERN-GUIDED COLD-START
To internalize thinking patterns for deepfake detection, we first employ a pattern- -uiieed cold-start. Different from common practice, we involve two steps: Supervised Fine-Tuning (SFT) for format injection, and Mixed Preference Optimization (MiPO) strategy to align the reasoning process.
SFT Pattern Injection. Suppose the SFT dataset is denoted as D1 {(q. - where the target output sequence including pattern-aware reasoning and final answer. & denotes input image and user query. The training objective maximizes the likelihood of generating s given input q:
T
L1 = -E(g.e)
Σ
log (8( q, 82r))
where Ty denotes the token distribution from the current model. In the following we introduce the construction process of our training data Dj-
To minimize human costs, we use MLLMs for automated annotation, similar to recent prac- tices (Huang al.) 2024 Xu al. 2024b). However, this encounters two challenges in case: (1) The MLLMS tend to overlook some subtle artifacts like abnormal optical focusing. (2) The model prioritizes producing logical paths than accurately locating artifacts. To mitigate the issues, we construct multi-step annotation pipeline. We first manually inspect a subset and summarize a comprehensive artifacts taxonomy (Figure (a)): (1) Perceptible structural anomalies. which are immediately visible and casy to detect. (2) Subtle low-level artifacts, which require careful in- spection. (3) Cognitive violations of physical laws, which are implicit and require connecting to common sense or real-world knowledge. Then, we decouple the annotation into three specialized yet coherent steps (Figure (b)), resulting in 36K samples for D,. Detailed process and prompt templates are provided in Appendix A.4 Annotated examples are presented in Figure (c).
MiPO Reasoning Alignment. To further facilitate human- human-aligned reasoning, we meticulously eu-
rate mixed preference dataset D, {(q. sw. s; ) ({ Sw: a ) Specifically, collect two types of non-preference data for the fake images: (1) the trajectories where the answer is correct but the reasoning content is not precise or detailed enough (i.e., 84), (2) the trajectories where the answer is incorrect (i.e., 8)²). 8w denotes preferred reasoning traces, which precisely annotated
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Stage 1: Pattern-Suided Cold-Start
Dy SFT Set (36K)
Dy MIPO Set (3K)
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Figure 3: Overview of two-stage training pipeline. (a) For Pattern-Guided Cold-Start, we first employ SFT to internalize thinking patterns. Then we introduce MiPO to facilitate human-aligned reasoning. The MiPO dataset consists of mixed non-preference data, encouraging model to perform precise and fine teee-grained reasoning. (b) For Pattern-Awan GRPO, we introduce pattern-wwar re- ward to incentivize adaptive reasoning ability on pattern granularity, yielding VERITAS model.
by our human experts. Both S and s are sampled from the outputs of the SFT model, yielding 3K high-quality paired samples for dataset D2. Note that the images in D2 strictly come from the in-domain training set, without introducing any OOD samples. Suppose the SFT model is denoted as Ta the training objective for MiPO is formulated as:
(9log To( (g)
Team (s:| )
To(s) (q)
L2 - log
Te (swlg) B Blog
where (-) denotes the sigmoid function and |3 controls the strength that the model deviates from the reference model. As shown in Figure[] [ by learning from such mixed rejected traces, our model can perform more precise and fine- ane-grained reasoning compared to pure SFT cold-start.
4.2 PATTERN-AWARE EXPLORATION
After cold-start, the trained model possesses the fundamental reasoning capacities for deepfake de- tection. However, it still fails on more challenging samples. To mitigate this, we introduce Pattern- Aware GRPO (P-GRPO) to encourage the model to perform comprehensive reasoning and potential self-reftection. Unlike recent approaches (Tu et al. 2025 Xiao et al., 2025) that encourage adaptive reasoning through length reward, we suppose that absolute reasoning length is not critical. Instead, we incentivize appropriate thinking patterns through the aatre reward mechanism.
Suppose the training data for P-GRPO is D3 = {(g, ) where a denotes the binary answer. We randomly sampled 9K images from in-domain training set. For given query q, P-GRPO samples G responses {01, o2.... .G} using the current policy model Tea- The quality of each response {R1, Rg,... .... R } is evaluated through reward functions. Suppose the cold-started model Te is adopted as reference policy, the training objective is formulated as:
L3 = ((g.a))Do. ~o (:19)
G loul
-D'DKL(ToolTTonall (3)
min (O))(0)A....)) (1))
ΣE
|0,1

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Published as a conference paper at ICLR 2026
where
To( To (Oi, I, (i,))
I, ct,
As. - (o--mann(( (e Rcl))
(0)
(4)
R1 RG))
The reward R2 for each response is evaluated from three perspectives:
Pattern-aware Reward. Suppose C E {0, represents the correctness of the final answer, with
= 1 denoting the answer is right. P E {0. and R E {0, }} represents whether the reasoning involves "planning" and "self-reflection" respectively. The pattern-awas aware reward defined as:
2.0,
ifC=1 A (P=1VR=
1.0,
ifC=1 AP = R=0,
R Rpateen
0.0,
ifC=0 AP = R
(5)
0.5,
ifC=0 AP 11 R- 0-
1.0,
ifC=0 R=
Specifically, we encourage the model to reach correct answers through planning and self-reflection by assigning larger reward (i.e., 2.0) if they are involved in the reasoning process. However, if these patterns lead to incorrect answers, we impose a penalty for its overthinking. Since self-reflection is more decisive pattern, assign a larger penalty (i.e., -.00) for errors resulting from it.
Reflection Quality and Format Reward. To facilitate meaningful self-reflection, we assess the quality of reflection by external model M: Red M(S). The criterion is the originality of the reflection, ie., whether introduces new perspectives rather than restating prior discoveries. The model only obtains Ree when the answer is correct. For format reward Rymt predefine some combinations of reasoning patterns and set Rim = when the response conforms to valid formats.
Suppose I(-) is the indicator function. The final reward R for each response is defined
== Rpattem + \1 Ree I(C=1) + Rmn
(6)
In practice, given that only verifiable answers required, the training data D3 be freely panded. Our coar-start model serves as solid reasoning foundation, upon which the community can utilize custom data with P-GRPO to achieve more powerful reasoning model for deepfake detection.
5 EXPERIMENTS
5.1 EXPERIMENTAL SETUP
State-of-the-Art Methods. We trained 10 state-of-ther of-too--tthht-r- (SOTA) detectors on our dataset, includ-
ing F3Net (Qian al.
2020). UniFD (Ojha al.) 2023). IID (Huang al. 2023). FreqNet (Tan)
et al., 2024a). ProDer
Cheng et al. 2024). NPR (Tan et al.
2024b), AIDE Yan et al. 2024a).
Co-SPY (Cheng et al.
(ann al. 2024c). We also assess 4 open-source MLLMs of similar size to our model. including Qwen2.5-VL
2025), D (Yang al. 2025b), Effort
Bai et al. 2025),
IntemVL3--8B (Zuu et 2025). MiMo-VL-7B (Team 2025) and GLM-4. Thin-hhinnng (Hong ct al. 2025), along with 2 powerful closed-source models GPT-4o (Hurst et al., 2024) and Gemini- 2.5-Pro (Comanici et al., 2025). Besides, we evaluate recent MLLM-based forgery detectors,
cluding FakeShield (Xu et al. (022b), M2F2-Det (Guo et al.) 2025b). SIDA (Huang et al. (2025a).
LL (Wen et al. 2025c). FFAA (Huang et al.
2024). More details are in Appendix A.2
2024a Guo et al. 2025b), we take Accuracy (Ace) to measure model performance. Precision and Recall are reported in Appendix A.5
Metrics. Following previous works Zhang et al.
Implementation Details. We implement VERITAS with InternV1.3-8B (Zhu et al. 2025). For the cold-start SFT, we train the model for epochs using LoRA (Hu et al. 2022) (rank=128, a=256). The learning rate is set to trained for 2 epochs with the same setting of SFT. For P-GRPO, we further train the model for epochs with the same LoRA setting. The learning rate is set to 10 with batch size of 16. G SeeeReearr--we--333 Qwen-3B (Wang al. 2025d) the reward model M. For each stage, directly adopt model from the last step.
1 with batch size of 64. For cold-start MiPO, the model is
set to 4, with a temperature of 1.0. 3 and 3' are set to O. We take UnifiedReward

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Published as a conference paper at ICLR 2026
Table 1: Performance comparison (Acc.) on HydraFake dataset. In-domain (ID) results are aver- aged. To ensure fair comparisons with MLLM-based detectors, 1) we exclude ID set in their average results and 2) further restrict the training scope of our method to FF++, StyleGAN, StableDiffusion XL and FFHQ (similar to FFAA), yielding VVERITAS-MINI.. The best results are bolded and second best are underlined. More metrics in Appendix A.5
Cross Model
Cross NouseForgery
Cross-Domain
Cross- Domain
Method
ADF
PLAXX
Story
MAGI-
HART
lnbuity
SEGAN2
ICLight
CodeFF
Infinie V.
PaLID
FaceAda da.
Desplace
Infintey Y.
Drcuminea
HaibooA
GPT-40
FRW
Avg.
Small Vision Models
F3Net (0C
85.3
87.8
78.6
48.9
71.
84.9
85.5
55.6
68.6
66.2
66.4
73.2
UniFD (CVPR'23)
82.7
90.7
93.8
82.5
73.0
94.4
90.7
61.8
819
75.4
73.7
68.1
81.3
67.4
67.3
80.5
75.2
73.3
67.5
78.0
IID (CVP8'23)
83.3
82.8
80.0
82.2
53.3
79.7
81.8
81.8
63.3
63.8
64.2
72.4
FregNet (44
66.8
60.3
76.7
59.0
69.2
77.1
75.1
33.1
73.1
70.3
72.
77.4
67.7
50.6
67.0
62.1
59.3
58.3
51.2
64.6
ProDet (NIP5"24)
90.5
92.6
91.2
88.2
91.9
93.8
93.1
56.3
58.6
80.8
88.1
91.0
83.3
58.1
82.9
71.3
75.6
66.3
74.1
80.6
NPR (CVPR)24)
75.6
68.8
91.2
59.5
60.6
79.8
46.0
69.8
AIDE (25)
80.4
68.8
86.3
64.0
88.9
95-4
76.0
56.7
79.2
86.1
74.2
62.4
75.7
59.7
67.9
49.7
58.0
51.9
59.2
70.6
Co-SPY (CVPR'25)
93.5
95.5
23.1
95.3
92.5
88.6
90.6
79.1
82.5
74,0
79.5
84.7
D (CVPR 25)
87.3
93.6
95.6
91.3
90.7
95.8
95.5
62.4
71.6
82.9
80.0
82.4
73.7
69.7
74.6
78.1
70,9
80.8
64.3
81.1
Effort (ICML'25)
94.7
82.8
96.5
78.0
90.5
97.8
98.3
64.7
94.8
89.7
89.5
92.9
88.0
64.8
82.2
61.5
66.4
53.8
74.0
82.2
Generic MLLMs
Dwen2.5-VLL-7B
$1.2
50.0
50.0
49.7
50.0
$2.0
52.9
50.5
56.7
50.7
53.6
54.5
51.6
50.7
53.6
80.2
67.5
52.5
50.5
54.1
54.0
54.0
49.8
49.0
56.6
55.8
57.2
62.9
54.2
62.9
63.6
54.8
67.7
54.4
62.1
77.1
66.5
47.4
51.8
58.3
MiMo-VL-7B
63.8
74.5
77.1
82.5
60.3
8244
81.4
48.7
82.6
76.1
79.7
78.4
82.8
57.7
75.6
79.4
70.7
67.7
54.9
72.5
GL.M4.1 10MM--11-9Hhhiit V-99Think
56.4
55.2
52.3
50.5
51.6
68.4
60.7
54.3
68.4
63.3
65.7
55.1
81.0
58.7
72.7
83.7
69.2
52.0
53.9
61.7
GPT-4o
57.7
52.0
54.8
52.5
61.4
58.0
60.8
Gemini-2.5-Pro
72.2
64.9
92.4
82.8
62.5
93.4
93.2
73.7
83.3
87.4
85.5
84.7
85.6
67.2
75.6
87.5
82.4
70,9
53.0
78.9
MLLM-baved Forgery Detectors
M2F2-Det
56.0
57.7
55.4
65.5
80.0
57.4
73.0
67.2
50.6
53.0
70.6
63.2
TakeShield (FCLR 25)
64.3
64.0
61.5
63.1
61.8
63.3
64.0
57.3
60.9
58.1
63.6
63.7
50.2
83.8
53.8
51.3
53.9
55.6
60.8
SIDA-7B (CVPR
97.3
97.7
79.5
95.0
60.6
62.3
89.7
94.4
80.0
78.0
68.9
57.3
76.3
SIDA-138 (CVPR22)
80.7
78.5
54.8
52.5
91.3
82.4
63.7
61.2
68.2
56.7
67.1
84.3
60.8
58.2
88.3
74.0
74.1
59.9
69.8
FFAA (Arxir 22))
55.1
50.9
72,9
63.5
60.8
57.6
52.7
70.9
71.8
58.4
62.4
86.0
67.7
58.4
55.3
59.2
49.6
68.3
61.0
FakeVLM (WPP
78.2
78.5
76.8
76.2
76.2
76.9
76.5
75.7
78.7
74,5
77.3
VERITAS-MINI
95.5
99.1
97.3
72,8
97.0
96.1
82,5
76.3
90.0
83.7
82.9
79.3
72.5
78.7
92.0
93.0
85.5
70.6
85.8
VERITAS (cold-start)
96.8
79.5
99.5
96.0
99.9
99.7
99.9
84.0
65.5
94.8
86.2
93.4
86.7
55.9
73.5
93.7
89.3
88.1
76.4
87.1
VERITAS (ours)
97.3
94.8
99.8
99.9
90.3
75.7
97.9
91.8
95.1
92.3
89.2
78.5
90.7
Table 2: Effect of the proposed Table 3: Ablations on the reward Table 4: Ablations on different functions in P-GRPO.
pattern-aware reasoning.
base models and model sizes.
Model
ID
CM
CF
CD)
Riurers Bust Reve Ros
ID
CM
CF
CD)
Base Model
1D>
CM
CF
CD)
w/o Reasoning
97.8
93.3
73.0
69.5
97.2
96.4
86.3
79.9
QwenZ, 5-VL-BB
96.8
97.7
89.0
81.4
Post-hoc Explanation
96.3
95.0
79.0
768
97.0
97.3
87.0
80.7
MiMo-VI-7R
93.0
83,0
Flexible Reasoning
96.2
94.3
81.2 7688
/
97.3
97.7
87.9
81.4
IntemV113-28
97.4
97.4
87.3
80.4
Pattern aware Reason.
96.9
98.4
87.4
80.1
97.3
98.6
90.3
822
IntemVL3-SB
97.3
98.6
59.3
82.2
Flexible Reason.
+0.7
+4.1
+6.2
+3.3
A GRIO
+0.1
+2.
+4.0
+2.3
IntemoVL33114B
98.5
99.3
92.2
82.6
5.2 MAIN RESULTS
Comparison to SOTA detectors. As shown in Table our VERITAS model achieves SOTA per- formance on four evaluation scenarios, achieving 6.0% averaged gains over the previous best. Ex- isting detectors show great performance on cross-model split (over 90% for D but fall short on cross-forgery and cross-domain scenarios (mostly less than 85%). VERITAS mitigates the gap, achieving over 90.0% accuracy on unseen forgery such as face restoration and personalization, and over 90.0% on in-the-wild data from Dreamina and 89.2% on GPT-4o. The cold-start model also achieves promising results, but without incentivizing planning and self-reflection, the cross-forgery results are degraded. More results and analyses can be found in Appendix A.5
Comparison to SOTA MILMss Compared to our base model, VERITAS achieves 32.4% averaged gain, suggesting the effectiveness of our training strategy. The models with similar sizes show limited abilities for deepfake detection, with less than 60% accuracy. Gemini-2.5-PP shows the best capacities among these MILMss, even outperforming some of the fine-tuned detectors. VERITAS surpasses Gemini-2.55PPr P-o by 11.8%, demonstrating great generalization.
Comparisons to MLLM-based detectors. For fair comparisons, we restrict our training scope (Table []. Even with limited data scope, VERITAS-MINI still outperforms existing MLLM-based detectors. indicating the effectiveness of the proposed framework. M2F2-Det and FFAA, though tar- geted at deepfake detection, suffer from poor generalization on HydraFake. SIDA-7B and VLM achieve promising results by contrast. Moreover, VERITAS exhibits certain ad advantages in both de- tection accuracy and reasoning depth (Figure G). More cases can be found in Appendix A.8
8

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Published as a conference paper at ICLR 2026
Table 5: Effect of reasoning patterns, SFT and P-GRPO are performed for comparisons.
Table 6: Ablations on non-preference in MiPO.
Model
ID
CM
CF
CD
Avg.
Model
ID
CM
CF
CD
Avg.
Flexible Reasoning
96.2
94.3
81.2
76.8
87.1
VERITAS
92.1
Pattem aweere-wwareeeeeee Reasoning
96.9
98.4
87.4
80.1
90.7
wlo MIPO
96.9
98.1
874
80.1
90.7
w/o <fast>>
97.3
98.8
86.9
79.1
90.5
MIPO (wlo o
96.9
98.6
89.2
81.4
91.5
<planingg>
96.7
96.9
85.0
80.1
89.7
<reflection
97.0
97.2
77.3
88.5
(wil of
58.6
6088
w/o <condlusoon
97.2
98.2
86.2
79.0
90.1
100
96.8
96.8
88.4
90.6
100
97.3
98.6
Anownr ThEnk
Oum (sl Cars Start
Fattem axtre Th
95.8
96.9
97.3
90.3
90.0
90.3
Results (9)
92
93.9
89.3
Results (9)
87.4
88
87.1
90.3
82.2
87.4
84
82,2
82.5
80.1
69.4
72.1
73.1
68.7
76
75.2
79.5
61.0
72
SFT
P-GRPO
In In-Domain
Cross-Model Cross- Cross-Forgery Cress-Demain
Figure 4: Ablations on the training stages. "Avg" is direetly averaged across four splits.
Figure: 5: Effect of Cold-Start. We compare differ- ent settings of pure RL.
5.3 ABLATION STUDIES
We provide primary ablations in main text. More analyses on the training protocol (A.5.2) results on recent benchmark (Li et al. 2025 A.5.2 selection of P-GRPO training data (A.5.4) and reward model (A.5.6), hyperparameters (A.555 and efficiency analysis (A.5.3) can be found in Appendix.
Effect of pattern-aware reasoning. As shown in Table 2 compare different reasoning paradigms using SFT and P-GRPO training. Although the improvements on in-domain datasets are marginal, our pattern-aware reasoning demonstrates clear advantages flexible reasoning on OOD scenarios, achieving 6.2% and 3.3% gains on CF and CD testing respectively. The post-hoc explanation adopted recent methods exhibits degraded performance in OOD testing, further veri- fying the superiority of pattern-awar aware reasoning.
Ablations on different training stages. As shown in Figure 4 we investigate the effect of each training stage. Applying MiPO or P-GRPO upon SFT model both achieve significant gains, with P-GRPO performing better, which is due to the online sampling and pattern-aware incentivization. Applying MiPO before P-GRPO yields the best performance, achieving 2.9% and 2. 2.1% gains on CF and CD testing respectively. This is because MiPO ensures high-quality rollouts in subsequent stage, facilitating more accurate policy updates for online RL.
Effect of Pattern-guided Cold-Start. As shown in Figure 5 we investigate different RL settings without cold-start. The training data keeps consistent with two-stage pipeline. Answer-only model achieves better ID results while incorporating thinking improves CM and CD performance. However, all settings underperform the model with cold-start. The low-quality explorations lead to unstable training. Results in Figure 4 further verify the effectiveness MiPO during cold-start.
Effect of Pattern-aware GRPO. As shown in Table our P-GRPO achieves noticeable improve- ments compared to original GRPO. Specifically, pattern-aware reward outperforms the vanilla accu- racy reward especially CF and CD scenarios. The reflection quality reward benefits both original GRPO and our P-GRPO, which demonstrates the importance of high-quality self reflection. In Ap- pendix A.5.4 we observe that by adding several "unseen" data in P-GRPO, the ODD performance can be further improved, demonstrating promising scalability with only binary labels required.
Effect of specific reasoning patterns. As shown in Table 5 "fast judgement" is helpful for CF and CD. but is not critical overall. "planning" is more effective on CM, since the fully synthesized images require a more holistic and structured analysis. "self-reflection" is critical especially on CF and CD, as it incentivizes the model to discover those unseen artifacts. "conclusion" provides certain gains, suggesting that synthesizing separate evidence into a coherent verdict is also important.
Ablations on the non-preference in MiPO. As shown in 6 6 helps improve the performance on CF (+1.3% and CD (+0.8%) scenarios. To understand the effects, we provide a qualitative in Figure 20 Without 8., the model still gets correct answers, but the analysis is superficial and less detailed, which causes certain failures unseen forgeries that might require in-depth reasoning.

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Published as a conference paper at ICLR 2026
Table 7: Evaluation of reasoning quality. Table 8: Robustness on Compression, Blur and Resize.
We utilize score and pairwise ELO rating. The results are averaged across all sets.
Score Evaluation
ELO
Method
Original
JPEG Compression
Gaussian
GPT-4o
Gemini 2.5-Pro
Rating
QF-90
QF=70
QF=50
MiMo- VL-7B
3,0731
2.5185
095.0
UnFDD
78.0
17.1
75.4
72.2
76.5
75.4
GPT-4o
2.4718
785.1
IID
72.4
71.4
70.1
69.7
72.1
71.9
Ge5inni2,5-PPo
4.1681
4,0070
966.9
ProDet
80.6
80.1
79.4
78.7
79.7
77.8
VERITAS (wl MiPO)
4.2538
4,1502
954.8
Co-SPY
84.7
84.5
83.4
81.0
82.7
77.0
VERITAS (w/ DPO)
4.5077
4.2863
1210.
Offort
82.2
73.1
79.6
66.3
79.1
70.4
VERITAS (w/ MiPO)
4.6479
4,4214
1359.0
VERITAS
90.7
90.1
88.7
87.4
88.8
84.3
Foleshrald (223) X
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This un ripcciio up serict fur class up chucf with car and cug is
foxtt mull other
netict
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lotian (partially cornect)
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Type par
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dlone fize
the
coold candoutt irans qpe dicely Ticttey her
Cenher Tyye stgecty parte badtaroo
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Visal tresee weweeeeeeedelll plop
Foruary rscaring The actacho facker fector spper
mnseeedee The add inarl The kor rosdution
tectbaat stuitig tipt dets punnd
Fake from InfiniteYou,
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Scneany givan imape jun't hompercnt based cbowe
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Thes foka maye The rmoge ediiets aederatas shatuctrr brcstitenna
scechaloce Ahough the vmgge wihllyy projects ortiericty, micitt monmat commasiin the everyy sere les she fecture agpest omriiiaal mage condncnnhlyy vitaeer
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sateyy revats hile noner throgh thas addle drangarcks d/aand
Figure 6: Reasoning comparisons between VERITAS and existing MLLM-based detectors.
5.4 FURTHER ANALYSES
Evaluation of reasoning quality. To evaluate the reasoning quality, we take two types of assess-
ments: (I) score evaluation which is based on predefined criteria (Figure (33). (2) Pairwise compar- ison which directly compares outputs from two models. We adopt MLLLM-as-a-Judde (Chen et al.) 2024a), using GPT-4o and Gemini-2.5-PP for evaluation. Similar to (Zhou et al. 2025). we ran- domly select 1K samples for evaluation. As shown in Table 7 our model achieves the best score and ELO rating, where MiPO greatly improves the reasoning quality. Moreover, our MiPO outperforms DPO in raising reasoning quality, which verifies the effectiveness of mixed non-preference strategy.
Different fine-tuned base models and model sizes. As shown in Table we adopt different MLLMs our base model. Intern VL3-SB outperforms Qwen2.5-VL-7B and MiMo MiMo-VL-7B. due to the dynamic high resolution strategy. VL3-2B achieves promising performance with fewer parameters, while scaling up 14B yields considerable gains on CM and CF scenarios.
Robustness evaluation. We investigate the performance under JPEG compression and Gaussian blur. Results in Table highhlgt the robustness of our model. Our model achieves consistently high performance under JPEG compression and maintains state-of-the-art results across different pertur- bations. Notably, this robustness is achieved without training on corresponding data augmentations such as random Gaussian blur, which instead are commonly adopted in previous methods.
CONCLUSION
In this paper, we introduce HydraFake dataset and VERITAS model. HydraFake introduces a holis- tic evaluation protocol to comprehensively measure the generalization capacities. We then train a multi-modal large language model (MLLM) based deepfake detector trained with our two-stage pipeline. Results HydraFake show that current detectors struggle on cross-forgery and cross- domain scenarios, while our model greatly mitigates the gap and is capable of delivering transparent decision process. We hope this work can inspire more generalizable and reliable deepfake detection.
10

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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work was supported by the Beijing Natural Science Foundation JQ23016, the Chinese Na- tional Natural Science Foundation Projects 62476273, 62406320, 62276254 and U23B2054, the Science and Technology Development Fund of Macau Project 0123/2022/A3, 0140/2024/AGI, 0044/2024/AG and 0084/2024/RIB2, and Ant Group.
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A
APPENDIX
The appendix is organized as follows:
Details of HydraFake Dataset.
A.I.I
Training Set.
A.1.2
In-Domain Evaluation.
A.1.3
Cross-Model Evaluation.
A.1.4
Crose-Forgery Evaluation.
A.1.5
Cross-Model Evaluation.
A.2
More Implementation Details.
A.3
More Discussions.
A.4
Multi-Step Annotation Pipeline.
A.5
More Experimental Results.
A.5.
More Results and Analyses HydraFake.
A.5.2
Cross Benchmark Comparison.
A.5.3
Efficiency Comparison.
A.5.4
Effect of Training Data in P-GRPO Stage.
A.5.5
Analysis of Hyperparameters.
A.5.6
Effect of Different Reward Model.
A.6
Full Prompt Templates.
More Qualitative Results.
A.8
More Qualitative Comparisons with Existing MLLM-based Detectors.
A.9
Failure Analysis of VERITAS.
A.1
Ethics Statement.
A.12
Limitations and Future Work.
A.1
DETAILS OF HYDRAFAKE DATASET
In this section, we provide more details about our HydraFake Dataset. We introduce the dataset from the perspective of training and evaluation protocols.
A.1.1 TRAINING
Real Images. HydraFake dataset contains real images from 8 public datasets. We extract 5 subsets as the training set, containing low-quality subsets and high-quality subsets. The low-quality images include FF++ (Rossler et al.) 2019), CelebA (Liu al., 2015) and LFW (Huang et al. 2008). The high-quality images include FFHQ (Karras et al. 2019) and CelebAHQ (Karras et al. 2017 This results in 24K real images for training.
Fake Images. In practical scenario, there are abundant fake images for training, while these images have attributes: (1) the quality of the images varies greatly, and (2) the forgery types often limited. To mimic such setting, we extract 21 subsets as the training set and strictly control the seen forgeries. We only include face swapping (FS), face reenactment (FR) and entire face generation (EFG) in our training set, leaving various forgery types unseen. Moreover, the deepfake methods in our training set are the latest, leaving fresh methods in the evaluation.
FS: FF++, BlendFace, FSGAN, SimSwap, FaceDancer, MobileSwap.
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Infinity
Cross Model
HART
IC-Light
CodeFormer
FaceAdapter
Cross Forgery
InfiniteYou
PuLID
Hailuo AI
Crass
Dreamina
Domain
GPT-4o
Figure 7: Examples of our self-constructed subsets in HydraFake datasets.
FR: FF++, Facevid2vid, Hallo, Hallo2, LivePortrait, AniPortrait, EmoPortrait
EFG: Dall-e 1, StyleGAN, StyleGAN2, VQGAN, Midjourney, Seeprettyface, Stable Cascade, Stable Diffusion XL, Attend-and-Exeon Excite.
A.1.2 IN-DOMAIN EVALUATION
For in-domain testing, we select subsets from the training set, and use the unseen identities as the testing samples. Specifically, make balance on the image quality and forgery types, choosing FS dataset FF++ (low-quality). two FR datasets Facevid2vid (low-quality) and Hallo2 (high- and two EFG datasets StyleGAN (Karras et al. 2019)) (high-quality and Midjourney (high-quality For low-quality subsets, the real images are sampled from FF++. For high-quality subsets, the real images sampled from FFHQ.
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A.1.3 CROSS-MODEL EVALUATION
The cross-model testing images come from deepfakes generated using unseen models. While the difficulty varies according to the model architectures. This includes 6 subsets, i.e., Adobe Fire- fly (Adobe) 2023). MAGI-1 [Sand-Al 2025), Flux1.1 Pro (Black Forest Labs 2024), StarryAl (Ai.
2023). Infinity (Han et al. 2025) and HART (Tang et al. (024).
Infinity. Infinity is Bitwise Visual AutoRegressive Model (VAR) capable of generating high- resolution and photorealistic images from textual prompts. Infinity redefines visual autoregressive model under a bitwise token prediction framework with an infinite-vocabulary tokenizer and bitwise self-correction. We reproduce the Infinity-88 8BB model, which is capable of generating 1024 x 1024 images. The control prompts generated using Qwen3-32B model, which leverages template- like sentence, balances between gender and age and avoids any semantic conflicts (c.g., wrinkles on the little girl's face) with the help of L.LM.
Hybrid Autoregressive Transformer (HART) is an autoregressive (AR) visual generation model capable of directly generating 1024 x 1024 images, rivaling diffusion models in image gen- eration quality. It contains a hybrid image tokenizer to improve the fidelity of the generated images. We reproduce HART model, which is based on Qwen2-VL-1.5B. The textual prompts are consistent with Infinity.
HART.
Adobe Firefly. Adobe Firefly is a proprictary suite of multimodal generative AI models developed by Adobe. It is built upon deeply customized and optimized diffusion model. It exclusively uti- lizes the Adobe Stock library, open-licensed content, and public domain works, thereby designed to ensure the commercial viability and mitigate copyright risks of its generated outputs. We collect this subset from WILD (Bongini et al. 2025), which is generated using template-like textual prompts.
AtarryAL. Starry Al is advanced generative artificial intelligence model engineered for high- fidelity text-to-image synthesis. Its core architecture integrates a transformer-basee encoder for the semantic interpretation of textual prompts with a latent diffusion model for the iterative synthesis of visual content. The model excels at translating complex, abstract descriptions into visually coherent and stylistically nuanced imagery. We collect this subset from WILD (Bongini al. 2025), which is generated using template-like textual prompts.
MAGI-1. MAGI-1 is an autoregressive denoising video generation model (Video AR) generating videos chunk- chunk-by-chune by-chunk instead of as a whole. It excels in generating high-quality, temporally con- sistent videos from text or image prompts. With support for large-scale model sizes and long context lengths, it is well-suited for a wide range of creative and generative video applications. We collect this subset from TalkingHeadBench (Bongini et al. 2025).
Fluxl.1 Pro. Flux1.1 Pro is currently the most advanced model of Flux series, which is introduced by Black Forest Labs. It is designed for fast, high-resolution and realistic text-to-image generation. We collect this subset from WILD (Bongini et al. 2025), which is generated using template- like textual prompts.
A.1.4 CROSS-FORGERY EVALUATION
The cross-forgery testing images come from deepfakes generated by unseen forgeries. With the rapid development of generative techniques, novel types of forgery are constantly emerging, such as portrait relighting (Zhang et al. 2025b) and IP-preserved personalization (Tiang et al.] 2025 Guo et al. 2024). To assess the model's 's generalization capacities when encountering these emerging deeptake methods, we collect representative forgery methods in our dataset, including face re- lighting (Zhang et al., (2055)), face restoration (Zhou et al., 2022). generative face swapping HHan) al.] 2024), facial attribute editing (Choi et al. 2020) and face personalization (Jiang et al. 2025 Guo et al. 2024).
Face Relighting. The method is based on IC-Light (Zhang et al. 2025b), which is an emerging ability in the generative models and is becoming prevailing. "IC-Light" means "Imposing Consistent Light", which is capable of adjusting the lighting sources and intensity in the image while keeping the subject highly unchanged. The condition is based on textual prompts. We sampled real images from FFHQ (Karras et al., 2019) and reproduced IC-Light to change the lighting condition of these real images. We implemented 10 lighting types (e.g., "sunshine from window".. "soft studio lighting"
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Table 9: Data list of the hierarchical evaluation protocol in HydraFake dataset.
Evaluation Split
Method
Sub-Type
Venue
Data Scale
Resolution
FaceForensics**
FS
ICCV
8,960
256 x 256
Facevid2vid
Arxiv' 19
2,000
256 x 250
In-Domain
Hallo2
FR
ICLR'25
1,660
256 256
StyleGAN
EFG
CVPR' 19
600
1024 x 1024
Midjoumey
BEG
None
600
1024 x 1024
Adobe Firelly
Proprietary
None
1024 1024
StaryyAI
Proprictary
None
600
1024 1024
Cross-Model
Flux1.1 Pro
Customized
None
600
1024 x 1024
MAGI-1
Video AR
None
1,048
256 x 256
HART
Image AR
Arxiv'24
4,200
1024
Infinity
Image AR
CVPR'25
4,200
1024 1024
StarGANv2
Editing
CVPR 20
2,000
256 x 256
CodeFormer
Restoration
NIPS'22
1,750
512 x 512
Cross-Forgery
IC-Light
Relighting
ICLR'25
2,082
1536 1536
FaceAdapter
Generative F$
BCCV'24
300
1024 X 1024
PuL.DD
Personalization
NIPS'24
3,360
1021 X 1024
Infinite You
Personalization
ICCV'25
3,244
1024 x 1024
Halluo
Commercial
None
1,000
256 1536
Dreamina
Social media
None
952
1024 x 1024
Cross-Domand
GPT-4o
Social media
None
630
159 ~ 1536
DeepFaceLab
Classic dataset
PR 2023
3,094
256 x 256
FFTW
Classic dutaset
CVPR'21
6,832
256 256
You-niteYuu-CD
Personalization
ICCV'25
2,960
1024 x 1024
and "neon light in city") and 4 lighting sources (i.e., "left", "right", "top" and "bottom"). We use multiple seeds for each condition, and then manually filter out those of low quality.
Face Restoration. The method is based on CodeFormer (Zhou et al. 2022), which can recover low- quality (e.g., blurred) natural faces to high-quality counterparts, even when the inputs are severely degraded. It can generate high-quality faces while maintaining the fidelity. In fact, this is helpful technique that has positive usage in many domains. But considering this can be also used for low- quality deepfake images, we take this as unseen forgery in our dataset. Specifically, we sampled some low-quality fake images from DF40 (Yan al., 2024d) and TalkingHHeadBnnh (Xiong al.) 2025). and then employ CodeFormer to restore them into 512 512 images.
Facial Attribute Editing. Facial attribute editing is a common manipulation, involving altering facial attributes such hairstyle and makeup. In our dataset we leave this type out for testing. We collect images generated by StarGANv2 (Choi et al. 2020) from DF40 (Yan et al. 2024d).
IP- (P-preserved Face Personalization. IP- preserved face personalization technology enables the gen- cration of synthetic faces that closely retain the distinctive visual attributes of original intellectual property (IP). By producing highly realistic and IP-consistent deepfakes, it facilitate unautho- rized exploitation or impersonation of protected characters and personalities. With the advancement of generative models, face personalization techniques are now capable of maintaining high-fidelity while following complex contextual and subject-specific instructions (e.g., transforming an ID photo into an image of the singer in the bar). We reproduce two timely methods PuLID (Guo et al. 2024) and Infinite You (Jiang et al. 2025). We sample real images from FFHQ as the source images. To enhance the realism and semantic coherence of face personalization, we employ Qwe-2.5-VLL-72B to generate customized prompts for each image.
Generative Face Swapping. The face swapping data in existing datasets are often produced by conventional approaches such as graphics-based methods GAN models. While nowadays the generative-based methods are capable generating high-fidelity swapped faces, which are based on Diffusion models. Considering the latest methods such as DreamID (Ye et al.) (025a) and Dy- namicFace (Wang et al. (025c) are not open-sourced yet, we implemented FaceAdapter (Han et al. 2024), which produces high-quality swapping data. We will keep tracking the ad vancements in these methods and update our dataset. The source faces are sampled from FFHQ. We manually filter out those low-quality generated images, only maintaining high-fidelity samples.
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A.1.5 CROSS-DOMAIN EVALUATION
The "domain" in our dataset mainly refers to data source. For instance, the cross-forgery data are generated using in-domain real images from FFHQ, which alters the manipulation methods but keeps the data source unchanged. But for cross-domain testing, the fake images are either generated from unseen real data srouce or entirely generated by commercial models. And we also crawled fake images from social media, which serves as a challenging cross-domain evaluation. Specifically, our cross-domain testing can be clustered into three types: (1) classic datasets, including DeepFacel.at from DF40 Yan et al. 2024d) and FFIW (Dolhansky al. 2019) which is widely adopted in existing benchmarks. (2) Reproduced deepfakes, including face personalization generated using real images from VFHQ (Xie et al. 2022). (3) In-the-wild deepfakes, where we collect data from social media such as Xiaohongshu and TikTok. We retrieved images through the tags of the posts and collected the images generated by GPT-4o (Hurst et al.) (2024) and Dreamina (team) (025a). and cropped out the digital watermarks. We further generate deepfake videos using Hailuo Al (team 2025b), and extract 8 frames for each video.
Valid Output Formats in P-GRPO
Format (Basic):
<fast> ... </fast>
<reasoning>... ... <reasoning>
<conclusion>... ... <conclusion>
<answer> <answer>
Format 2 (With Planning): <fast> .. </fast>
<planning>... <planning> <reasoning>... ... <reasoning>
<conclusion>... <conclusion>
<answer> ... <answer>
Format (With Self-Reflection): <fast>... ... </fast> <reasoning>... ... <reasoning>
<reflection>... ... <reflection>
<conclusion>... <conclusions
<answer> ... <answer>
Format 4 (With Planning and Self-Reflection) <fast> </fast>
<planning>.. ... <planning> <reasoning>... <reasoning>
<reflection>... ... <reflection>
<conclusion>... <conclusion>
<answer> ... <answer>
Figure 8: Valid output formats in Rime of P-GRPO.
A.2 MORE IMPLEMENTATION DETAILS
Training resources. Our model is trained with 8 PPUE GPUs based ms-swift İZhao et al, 2024). The theoretical peak computational capacity (TFLOPS) of one PPUE GPU is roughly half of an NVIDIA A100 GPU, and each PPUE GPU has 96GB VRAM. With such infrastructure, the SFT and MiPO stage take 5.5 hours and hours, respectively. The P-GRPO stage takes 11 hours on 9K training samples. All the inferences are conducted
single PPUE GPU.
Training details of previous methods. For previous SOTA methods, we reproduce them based on Deepfakelencc (Yan et al. 2023 For F3Net (Qia al. 2020), UniFD (Ojha et al. 2023
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IID (Huang al. 2023). Fre (Tan et al. 2024a), ProDet (Cheng al. 2024). NPR TTn al. 2024b) and Effort Yan et al. 2024c). we reproduce them based on DeepfakeBench Yan et al. 2023). For AIDE Yan et al. 2024a). Co-SPY (Cheng et al. 2025) and D (Yang et al.
2025b),
train the model with official codes and perform inference using DeepfakeBench. The images are first randomly cropped into 256 256 and then resized to 224 224. For Co-SPY (Cheng al. 2025), the images are resized to 384 x 384 following the official implementation. We apply a series of data augmentations during training, including random flipping, rotation, gaussian blur, brightness and contrast alternation, color jitter and JPEG compression. Following official guides, AIDE and D are trained for 100 epochs. FreqNet and NPR are trained for 50 epochs. The first stage of Co-SPY (i.e., artifacts and semantic encoders) are trained for 20 epochs, and the second stage (i.e., combination) trained for another 10 epochs. Effort is trained for 10 epochs and other methods are trained for 20 epochs. During testing, the images are resized to 224 x 224. For all these methods, we curate validation set containing 4K in-domain images for model selection.
Other details. The training data from all stages are strictly sampled from HydraFake training set. The 36K SFT data are randomly sampled and balanced across forgery types. The 3K MiPO pairs selected based on SFT models' outputs. 800 images that the SFT model fails to reach all correct answers under rollouts are selected. Each image is paired with 4 manually selected non-preference chains (from SFT model's outputs) and 1 manually annotated preference chain, resulting in 3K samples for MiPO. The 9K P-GRPO data are randomly sampled and balanced across forgery types. For open-sourced MLLMs, we provide prior knowledge and instruct the model perform thinking in the prompts. The full prompts are provided in Figure 33, Figure 39 and Figure 40 For Gemini- 2.5-Pro, we enable thinking and searching. A and A2 are set to 1.0 and 0.25, respectively. The valid output formats for R Ryms in P-GRPO are listed in Figure g
A.3 MORE DISCUSSIONS
Difference between explainable and reasoning deepfake detection. In this part, we formulate different task settings of MLLM-based deepfake detection. Given the input image I, deepfake detection aims to determine its authenticity y E {0, 1}, where means the image is fake and vice versa. Suppose the input image and query are collectively denoted as q. The sequential outputs of MLLM are denoted as 8 {81, 82, .... 8T where T is sequence length. The conditional probability of sequence is written as P(slq) = II ((s q, s-)))
Recent works (Chen et al. 2024b He et al. 2025) utilize MLLM for explainable deepfake detection, where LLM first generates the answer 8,4 (e.g.. "fake" or "this image is fake"). Then detailed explanation sequence 8g is generated based on (q, 8r} (where A < E). For simplicity, we suppose the answer $ is in single word. The process can be decomposed into:
A+8 II (=A+1
P(slg) II (()
S(Ate)
where the final decision (i.c.. II (())l))) is solely conditioned on the input image. This is fundamentally similar to the small vision models, where the distributional mapping f y is estimated directly within a single token, prone to overfitting.
In contrast, we formulate the deepfake detection as a reasoning task. The MLLM first conduct holistic reasoning, denoted as sR, and the final answer is then determined in s
R
R+A II I=++1
P(slg) II P(s. 8CR)
P(stlg,8<RtA)
(8)
where the final answer (i.e., I P(s: s<)+A)) is building on inputs and reasoning process. The mapping is altered into f' R ), enabling more comprehensive and adaptive modeling.
Large Reasoning Models. Large Language Models (LLMs) are inherently good reasoners for gen- eral tasks. A simple prompt engineering could activate the reasoning behaviors of LLMs (Wei et al.) 2022 Kojima et al. 2022), which is termed as Chain-of-Thousee
(CoT). Building the impressive capabilities of CoT, the community began to explore large-scale and structured reasoning. leading to powerful reasoning models (Jaech al. 2024 Guo et al. 2025a Xu et al. 2024a) through tailored
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Figure 9: Construction pipeline of Pattern-Aware SFT data. (a) We first inspect a subset and summarize the artifacts into three clusters. (b) Then we introduce multi-step strategy to generate pattern-awar reasoning data. (c) Annotated examples. The reasoning process evolves in complexity and depth (as highlighted in red), culminating in final answer through synthesis of all evidence.
post-training. For instance, DeepSeek-R1 (Guo et al. 2025a) adopts reasoning cold-start followed by Reinforcement Learning with Verifiable Rewards (RLVR) to incentivize the general reasoning capabilities. Inspired by the success of RLVR, recent works (Liu et al. 2025 Zhang et al. 2025ap
attempt to introduce pure rule-based RL into multimodal domain. However, a recent study ( (Yue) et al. 2025) points out that pure RLVR can not introduce novel abilities to base model. We also empirically reveal the suboptimal performance achieved by pure RI. Therefore, we first introduce
high-quality cold-start (Liao al. 2025 Chen al. 2025a Team et al., (025) to internalize the thinking patterns to the base model. Unlike general tasks that require diverse and flexible thinking patterns (Zhan et al., 2025), deepfake detection is a well-defined task. Therefore, we establish unified reasoning framework to facilitate efficient thinking.
Rationale for reasoning in deepfake detection. A possible concern is that even humans can fail on some highly realistic deepfakes. Applying reasoning for deepfake detection also faces the same problem. However, we point out that there is physiological limit on human perception. The human excels at high-level semantic reasoning, such as judging contextual plausibility, but is less equipped to detect subtle, low-level digital artifacts like subtle blurriness or unnatural texture patterns. con- trast, the machine can be trained to perceive these subtle artifacts with superhuman accuracy. The primary challenge, which traditional detectors face, is not perception but generalization, i.e., they tend to overfit to specific artifact patterns. This is where the pattern-aware reasoning becomes cru- cial. Our goal is not to mimic a human's 's intuitive guess, but to emulate a forensic expert's 's systematic investigation. Our approach uniquely combines the machine's 's superhuman perception with struc- tured and human-like reasoning framework (e.g., planning, reasoning, self-reflection, conclusion). As illustrated in Figure 21,, the model can perceive subtle artifacts (e.g., barely noticeable blurri- ness and faint texture anomalies) that are nearly imperceptible to the human. Therefore, reasoning crucial not to replicate the fallible human eye, but provide a logical structure that effectively leverages the model's perceptual abilities for robust generalization.
A.4 MULTI-STEP ANNOTATION PIPELINE OF SFT DATA
As shown in Figure 9 for fake images we divide the annotation process into three steps:
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Step-I: Based on human inspection, we found that most fake images exhibit 2 to 3 types of artifacts, hence we aim to find the two most prominent artifacts. To reduce model bias, we employ an ensem- ble voting strategy, leveraging Qwen2.5-V V
VLBBBB (Bai et al. 2025), Kimi- VL-A3B- -hinkiini (Team et al. 2025) and Intern VI.3-78B (Zhu et al. 2025) to sample 5 times individually. Only the answers that receive more than 10 votes selected, ensuring the reliability of the final results.
Step-II: In this stage, we aim to extract visual details that conform to the identified artifacts. We found that Qwen2.5-VL-72B performs better than GPT-4o (Hurst al., 2024) here, capable of generating detailed and factual responses. This yields concrete explanatory texts, like recent prac- tice (Wen et al., 2025c Gao et al., 2025] Zhang et al. 2025c). However, such plain explanations lack human- reasoning logic, which hinders the generalization to OOD samples.
Step-I1I: To emulate human mindset, we further transform the above explanations into logical chains. We define five "thinking patterns" and instruct the model to rewrite the explanations into different tags strictly based on the original meaning. We observed that large reasoning models are inherently adept generating highly logical content. Therefore, we use (wen3-235B--221 (Yang) et al. (025a) for this step, yielding high-quality reasoning data. Finally, the data undergo a filtering process, which involves rule-based filtering and balancing among different forgery types.
For real images, we only use the last two steps (without the need for anomalies detection). For Step- I we provide the full artifacts list Qwen2.5- V---2BB for comprchensive visual facts forensics. For Step-II we adopt Qwen3-235B-A A22B to convert the explanation texts into patter- reasoning chain. Specifically, for some low-quality real images, which may contain some misleading artifacts like unexpected blurriness or missing visual details, we instruct the model to point this out and put them in the "self-reflection" content. However, the model is not capable of directly perceive such minor artifacts especially when told the image is authentic. Hence, we provide a rough difficulty information based on dataset level and encourage the model to perform self-reflection on those diffi- cult images. This can mitigate the problem while it can not be fully addressed due to the significant loss of details in those low-resolution images.
A.5 MORE EXPERIMENTAL RESULTS
A.5.1 MORE RESULTS AND ANALYSES ON HYDRAFAKE
We provide full experimental results for all subsets. To help better understanding the model perfor- mance, we report Precision and Recall for each subset, with fake being the positive label.
The in-domain results are shown in Table TO Effort achieves the best results among previous de- tectors. It is worth noting that, under the mixed training sources, previous methods even struggle on in-domain datasets, i.e., most methods achieve less than 90% average performance. This is mainly due to the degraded performance on low-resolution datasets such as FF++ and Facevid2vid. We suppose this is major deficiency in current deepfake detectors, which tend to bias towards image resolutions. Our model achieves better performance, achieving over 99.5% on those high resolution images, while the results low resolution subsets still have room for improvement.
The cross-model results are shown in Table 11 D achieves the best results among previous detec- tors. Lots of previous methods achieve good performance on cross-model scenarios, with averaged performance greater than 90%, such as Dd, ProDet, Co-SPY and Effort. These models show great performance on cross-model data, especially on VAR architectures, achieving over 95% accuracy. Our model demonstrates extraordinary generalization performance on moosl data, achieving almost 99% accuracy, while the recall capacity on proprietary models (e.g., Adobe Firefly and Star- Al still has room for improvement.
The cross-forgery results are shown in Table 12 Effort achieves the best performance among previ- ous detectors. The performance of most detectors are limited. For instance, those detectors tailored for deepfake facial images (i.e., IID and ProDet), showing extremely limited recall capacities when encountering facial attribute editing and face relighting. Most methods achieve moderate perfor- mance on face restoration and personalization. These results verify that current detectors exhibit limited abilities to generalize unseen forgeries. Besides, it is worth noting that Effort achieve excel- lent performance on face relighting, greatly surpassing our method. We suppose the reason is that Effort freezes CLIP's semantic encoder, allowing the model to focus solely on detecting whether an
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Table 10: Performance comparison on the In-Domain (ID) subset of HydraFake dataset. The best results are bolded and the second best are underlined. We report Accuracy (Acc.), Precision (P.) and Recall (R.) and the averaged results (Avg.) are reported in Accuracy.
FaceForensics++
Facevid2vid
Hallo2
StyleGAN
Midjourney
Method
Avg.
Acc.
P.
R.
Acc.
P.
R.
Acc.
R.
Avc.
R.
Acc.
P.
R.
F3Net (ECCV"20 20)
84.5
80.5
90.9
89.6
83.6
98.6
85.0
78.3
97.0
82.5
77.4
91.6
84.8
78.6
95.6
85.3
UniFD (C)PR'23)
73.8
80.4
62.8
81.7
83.2
79.6
77.3
88.8
62.5
91.5
97.6
86.3
90.6
81.0
82.7
ITD (CVPR'28)
83.8
86.6
79.9
92.8
89.5
97.0
79.1
72.2
94.6
80.7
72.1
100.0
80.5
71.9
100.0
83.4
(e (4AA/240)
52.2
51.2
94.4
54.5
52.3
100.0
71.2
66.7
84.5
77.5
69.8
96.6
78.8
70.8
98.0
66.8
ProDet (4)P5'24)
84.8
82.2
88.8
84.6
100.0
89.0
94.4
88.7
97.3
88.4
99.0
90.5
NPR (4)
59.6
56.5
84,1
66.6
60.5
95.7
74.3
87.2
57.0
88.8
91.7
85.3
88.7
92.9
83.6
75.6
(ICLR'2)))55))
58.9
58.7
59.6
78.6
70.3
99.0
84.5
92.4
75.2
86.8
93.1
99.0
93.0
92.7
93.3
80.4
Co-SPY (CVPR'255)
71.3
76.0
62.3
89.3
82.9
99.1
82.1
91.6
70.7
95.0
92.2
98.3
94.0
94.0
86.3
D2 (C)
72.5
70.3
77.8
82.1
74.7
96.9
91.1
91.7
90.3
96.3
94.2
98.6
94.7
91.3
98.6
87.3
(ICM2
92.9
94.4
91.2
96.4
94.6
98.4
95.9
95.3
96.5
95.0
97.5
92.3
93.5
96,1
90.6
94.7
Qwen2.5-VI-77
51.3
93.5
2.8
51.5
94.3
3.3
50.0
50.0
1.1
51.5
80.0
4.0
51.5
76.4
4.3
51.2
VL3-m8
55.9
59.0
53.6
54.6
83.8
15.66
66.6
8.0
69.4
8.3
MiMo-VL-78
56.2
55.3
64.0
62.2
59.4
77.1
60.4
63.9
47.6
66.4
69.3
58.6
73.6
72.0
77.3
63.8
GLMM-4.V-9BThine
58.0
64.0
36,3
59.6
65.6
40.0
54.2
89.6
9.4
56.0
87.5
14.0
54.3
80.9
11.3
56.4
GPT-4o
49.2
20.0
0.5
53.0
84.2
8.0
49.8
33.3
0.5
63.2
100.0
26.5
52.5
91.6
5.5
53.5
Gemini-2.5.Pro
62.0
77.9
33.5
53.2
66.6
13.0
80.9
42.5
89.3
100.0
85.8
73.7
76.6
72.2
VERITAS (ours)
90.9
90.2
91.7
96.1
92.7
100.0
99.8
99.6
100.0
99.8
99.6
100.0
100.0
100.0
100.0
97.3
Performance comparison on the Cross-Model (CM) subset of HydraFake dataset. The best results are bolded and the second best are underlined. We report Accuracy (Acc.), Precision (P.) and Recall (R.) and the averaged results (Avg.) are reported in Accuracy.
Table 11:
Method
Adohe Firefly
PLUXLL1PM
StarryAI
MAGL-1
HART (VAR)
Infinity (VAR)
Avg.
Acc.
P.
R.
Acc.
R.
Acc.
P.
R.
Apc.
P.
R.
Acc.
F.
R.
Acc.
P.
F3Net (ECCV 20)
86.7
80.0
97.7
87.8
80.4
100.0
78.6
76.2
83.3
85.0
78.6
96.2
$6.0
78.6
99.0
62.9
78.6
90.4
B1.5
UniFD (CVPR
90.7
92.9
88.0
91.7
88.8
14.3
73.0
56.1
94.4
97.0
90.7
88.2
87.5
IID (CVPR125)
83.3
75.0
100.0
82,8
74.4
100.0
80.0
72.1
97.6
80.2
72.6
96.5
81.1
72.6
100.0
82.2
73.7
100.0
81.6
ToojNe14AAA/2
60.3
59.5
64.3
76.7
68.4
99.0
59.0
59.1
58.3
69.2
65.5
81.1
77.1
69.4
96.7
75.1
68.7
92.3
69.6
(NIP5
92.6
85.3
98.3
94.2
89.5
100.0
88.2
88.0
88.3
91.9
87.5
97.7
93.8
89.0
99.8
93.1
88.7
98.6
92.3
NPR (CVPP'22 24)
68.8
86.0
45.0
91.2
91.5
90.5
59.5
78.8
26.0
82.6
91.9
71.5
91.3
93.1
89.3
84.0
92.6
73.8
79.6
AIDE
68.8
85.5
43.3
86.3
91.0
86.6
33.0
88.9
93.6
83.6
95.4
94.3
96.6
76.0
57.7
79.9
Co-SPY (CVPW125)
93.5
93.6
93.3
95.5
94.1
97.0
85.3
93.4
76.0
93.3
92.2
72.7
96.6
94.5
9
95
94.1
96.7
93.3
D) (CVPR'25)
93.6
90.3
97.7
95.6
92.3
99.6
92.2
90.3
90.7
90.5
91.0
95.8
99.9
95.5
98.3
93.8
Elfort (FCML/25)
82.8
97.1
67.6
96.5
96.0
97.0
718.0
94.2
59.6
90.5
96.5
84.1
97.8
97.2
98.5
98.3
97.3
99.4
90.7
Qwen2.5-V1..
50.0
50.0
1.3
50.0
50.0
1.3
49.7
0.0
0.0
50.0
50.0
0.9
$2.0
83.8
4.9
52.9
87.3
6.8
50.8
IntemVL33-BB
54.0
74.0
12.3
49.8
47.3
3.0
49.0
28.5
1.3
56.6
83.5
16.4
55.8
83.1
14.5
57.2
84.5
17.6
53.7
MiMo-VLL-BB
74.5
74.7
74.0
77.1
76.7
78.0
82.5
68.0
55.3
60.3
64.0
47.1
82.4
77.9
90.6
81.4
77.2
89.2
76.4
GL.M-4.IV.9Thhi.
55.2
13.0
76.9
6.6
66.6
51.6
68.4
38.2
60.7
22,5
56.5
GPT-4o
57.7
96.9
16.0
52.0
83.3
5.0
$1.4
85.7
3.0
59.9
80.0
26.4
81.2
92.5
67.9
54.8
87.5
10.6
59.5
Gemini-2155PPt
41.2
90.3
92.3
72.0
62.5
34.1
93.4
93.2
98.5
81.5
VERITAS (ours)
94.8
99.6
89.9
99.8
99.6
100.0
97.0
100.0
94.0
99.9
99.8
100.0
99.9
99.8
100.0
99.9
99.8
99.9
98.6
image has been manipulated, which is critical in detecting relighting where the identities and other semantics are largely unchanged.
The cross-domain results are shown in Table 13 Co-SPY achieves the best results among previous detectors. Most methods including ours achieve degraded performance. Specifically, previous meth- ods almost fail on all cross-domain subsets, while our method still achieves robust performance on in-the-wild forgeries (e.g., 92.3% on Dreamina and 89.2% on GPT-4o). The performance on Deep- FaceLab is extremely limited. Different from cross-forgery and cross-model scenarios, the poor performance due to low Precision. Similar problem also exists in previous detectors. This means those unseen low resolution real images are hard for model to distinguish. Overall, our model strikes great improvements on cross-domain scenarios.
Besides, the zero-shot MILMSs tend to classify facial images into real photographs. Even GPT-4o fails on many cases, exhibiting extremely low recall (i.c., less than 10%). Gemini-2.5-Pro demon- strates strong capacities for deepfake detection, especially on high-resolution images, even beating most fine-tuned specialized detectors. Aggregating the above observations, we can find a intutive but interesting phenomenon: the MLLM-based detectors (especially reasoning MLLMs) are good at analyzing nigh-resolution images (typically over 512 x 512) but fall short on low-resolution coun- terparts. Once the MILMS can "see" the image details, they are able to make accurate judgments
26

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Published as a conference paper at ICLR 2026
Table 12: Performance comparison on the Cross-Forgery (CF) subset of HydraFake dataset. The best results are bolded and the second best are underlined. We report Accuracy (Acc.), Precision (P.) and Recall (R.) and the averaged results (Avg.) are reported in Accuracy.
Method
SturGANv2
IC-Lighh
CodeFormer
Infinite
PuLID
Face-dapter
Avg.
Acc.
P.
Acc.
Acc.
P.
Ace.
F3Net (ECCV
41.3
20.7
6.2
48.9
47.9
24.8
71.9
72.8
69.8
84.9
78.2
96.8
85,5
79.5
95.7
72.6
77.5
62.8
67.5
UniFD (CVPR'23)
61.8
81.7
30.4
81.9
89.1
72.6
75.4
88.0
58.7
73.7
87.0
55.6
68.1
83.6
45.1
81.3
90.3
69.6
73.7
IID (CVPR
41.4
32.8
16.5
53.3
54.3
41.0
79.7
72.9
94.4
8188
73.4
99.6
81.8
73.3
99.9
73.7
68.2
87.1
68.6
PregNet (MMA224)
33.1
11.4
5.0
73.1
67.6
88.9
70.3
66.6
81.5
72.8
67.4
88.3
77.4
69.9
96.2
67.7
64.9
75.0
65.7
ProDet (NIP5'24)
56.3
24.5
34.4
85.8
91.0
86.0
76.4
NPR (CV
47.7
23.6
2.1
67.8
85.2
43.2
60.6
78.3
29.3
79.8
99.1
66.0
89.0
92.8
84.5
67.7
81.9
41.9
65.8
AIDE (2CLR'25
56.7
20.3
79.2
91.5
64.3
90.8
74.2
55.0
62.4
83.9
30.5
75.7
90.3
72.4
Co-SPY (CVPR
77.0
91.1
59.9
92.5
93.8
91.0
88.6
90.3
86.5
90.6
93.2
87.5
79.1
89.9
65.5
87.3
88.7
85.4
85.9
1 (CVPR'25)
62.4
82.4
31.5
71.6
86.3
51.4
82.9
90.9
73.1
80.0
188
68.7
82.4
89.8
73.1
73.7
84,1
57.4
75.5
Effort (ICM) 25))
64.7
93.2
31.7
94.8
96.3
93.2
89.7
97.1
81.9
89.5
96.0
82.3
92.9
96.2
89.2
88.0
95.0
80.2
86.6
QwenZ S-VL-77
50.5
64.7
222
56.7
90.8
15.2
50.7
70.0
2.4
53.6
901.3
8.1
54.5
9014
10.1
51.6
63.6
4.7
$2.9
62.9
30.0
77.1
12.0
86.9
63.6
92.1
29.7
12.7
96.3
61.0
VL-
48.7
47.7
26.2
82.6
76.4
94.3
76.4
75.1
78.9
79.7
76.3
85.7
78.4
75.8
83.5
82.8
79.1
88.3
74.8
GLM-4.V.9BThink.
54.3
68.4
94.7
39.0
95.7
27.8
66.7
32.6
87.4
11.9
97.9
64.6
GPI-46
66.4
82.6
41.5
58.9
100.0
18.0
52.5
91.6
5.5
64.4
100.0
29.0
60.9
94.0
23.5
55.5
100.0
10.1
59.8
Gemini-2.5-Pro
73.7
89.7
53.5
83.3
89.7
75.1
87.4
86.0
89.3
85.5
86.2
84.5
84.7
88.4
80.0
85.6
85.3
85.8
83,4
VERITAS (ours)
90.3
99.5
81,0
75.7
99.5
51.5
97.0
98.6
95.3
91.8
98.9
84.5
95.1
99.5
90.5
91.7
98.6
84.6
90.3
Table 13: Performance comparison the Cross- (CD) subset of HydraFake dataset. The best results are bolded and the second best are underlined. We report Accuracy (Acc.), Precision (P.) and Recall (R.) and the averaged results (Avg.) are reported in Accuracy.
DeepFaceLaa
You-niteoo-CCD
Dreamina
Hailuo
GPT-4o
FFIW
Method
Avg.
Acc.
P.
Ace.
P.
Acc.
R.
Acc.
P.
R.
Acc.
P
Apc.
P.
R
F3Net (ECCV
$7.7
$4.7
89.1
78.5
72.6
91.6
55.6
55.0
60.9
68.6
63.3
$8.6
66.2
63.6
75.5
66.1
61.8
71.4
65.5
UniHD (CVPR'22)
67.4
79.2
67.3
50.5
71.2
75
73.3
72.7
65.4
74.5
IID (CVPR
65.2
65.1
65.5
69.9
63.0
95.9
63.8
58.0
99.6
63.3
57.6
100.0
63.8
58.2
97.8
64.2
66.6
56.6
65.0
FregNet (AAA/24))
50.6
98.7
67.0
85.8
58.4
83.4
56.9
58.3
73.3
95.0
ProDet (NIPS 24)
$8.1
54.6
95.3
82.9
81.3
85.4
71.3
71.3
71.4
75.6
71.2
86.0
66.3
09.9
57.4
74.1
73.0
76.5
71.4
NPR (CVPR'24)
52.6
52.4
56.6
73.0
N2.1
58.7
76.6
80.9
69.5
62.3
70.7
42.0
50.3
50.4
20.9
46.0
46.3
50.9
60.1
AIDE
59.7
57.5
73,9
67.9
75.2
53.2
19.7
49.4
25.2
58.0
60.6
45.8
51,9
53.0
33.3
59.2
57.3
71.2
57.7
Co-SPY (CVPR'25)
67.6
63.8
81.2
80.0
85.1
72.7
82.5
80.7
85.3
74.0
75.7
70.6
79.5
78.7
80.9
64.3
64.2
64.7
74.7
D' (25PR'25)
69.7
70.0
69.1
74.6
78.8
67.2
78.
72.2
91.4
70.9
67.7
79.8
80.8
75.1
92.0
64.3
66.0
58.7
73.1
Effort (ICM//25)
64.8
59.1
96.3
82.2
75.4
95.5
61.5
57.6
86.5
66.4
60.1
97.4
53.8
52.9
69.3
74.0
68.3
89.7
67.1
Qwen2.5-VL--78
50.7
71.7
2,4
53.6
92.8
80.2
99.4
60.7
67.5
100.0
35.1
52.5
100.0
50.5
56.1
4.5
59.2
IntemVV33-BB
54.4
55.5
44.1
67.1
83.6
42.7
77.1
85,4
65.3
66.5
78.9
45.0
47.4
37.5
7.6
51.8
52.0
45.1
60.7
MiMo-VLL-BB
$1.7
56.4
68.6
75.6
73.7
79.6
79.4
73.5
91.9
70.7
68.3
76.5
67.7
67.1
69.5
54,9
54.8
56.2
67.7
GLM-LLV-9BThink
58.7
82.6
22.0
72.7
95.1
47.8
83.7
96.0
70.3
69.2
92.1
42.0
52.0
68.4
8.2
53.9
60.8
21.7
65.0
GPT-4o
49.4
41.7
2.5
62.0
98.0
24.5
90.7
98.8
82.4
73.7
100.0
47.5
58.0
94.4
17.0
52.8
59.3
17.8
64.4
2.5-ii--2.--Pro
67.2
72.4
55.6
75.6
88.7
59.0
87.5
89.5
84.9
82.4
95.2
70.9
43.2
53.0
67.6
11.5
72.8
VERITAS (ours)
58.6
54.7
100.0
84.1
94.1
72.8
92.3
90.0
95.1
90.2
86.5
95.2
89.3
86.3
93.2
78.5
76.1
83.0
82.2
and provide human-aligned reasoning process. Conversely, small vision models exhibit certain ad- vantages on low-resolution images, as such data is more suited for distribution modeling. Therefore, collaborative system of MLLMs and small models could be a promising future direction.
A.5.2 CROSS BENCHMARK COMPARISON
In Table 14 we provide a cross benchmark comparison. We select the facial subsets from AI- GIBench (Li et al. 2025). Note that these subsets also remain unseen in our HydraFake training set, which serves as an OOD testing of our method. Since the real splits contain images of common objects, we substitute these images with facial images from VFHQ. To investigate the impact of training sources, we also train existing methods on FF++ (Rossler et al. 2019) similar to previous one-to-many setting. The quantity of training samples for FF++ and HydraFake are kept consis- tent (both are 48K). Specifically: (1) our HydraFake-CD is more challenging than AIGIBench, with the best result being 6.7% lower (82.2% vs. 88.9%) and the second best result showing 10.0% de- crease (74.7% vs. 84.7%). (2) On broader datasets, recent AIGC detection methods (e.g.. Co-SPY and Effort) still demonstrates clear advantages to the methods tailored for deepfake (e.g.. IID and ProDet). This may indicate that specialized modules for facial images may struggle to generalize well to modern fully synthesized and high-fidelity deepfakes. On these data, concurrently model- ing the semanties and artifacts (like Co-SPY and Effort) may be more effective. (3) Expanding the
27

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Published as a conference paper at ICLR 2026
Table 14: Cross benchmark comparison. Performance (Acc.) on AIGIBench (Li et al. 2025 and the HydraFake-CDD set. For previous methods, we implement two settings: (1) train on FF++ (Rossler) al., 2019) similar to previous setting, and (2) train on HydraFake dataset that contain multiple sources. The quantity of training samples for FF++ and HydraFakke are kept consistent. The per- formance of recent methods increase when trained on more diverse sources (highlighted in gray),
while similar gains are not observed for deepfake detection methods (highlighted in blue).
HydraFake-CD
AIGIBench
Method
Training
Despface
Intinitey.
Dreanina
Hoilao Al
GPT-4
FFI
Avg.
BLIP
InfiniteD
InSwap
IPAdsprer
REGAN
Stylewim
WFR
Avg.
F3Net (EC)
FF++
52.9
56.4
63.8
76.0
69.5
52.6
61.9
65.6
44.1
51.8
52.5
72.7
$1.1
47.1
46.1
$3.9
UniFD (3V
FF++
72.9
54.3
63.0
59.2
57.6
66.9
62.3
51.3
80.7
77.1
63.5
67.3
67.1
78.3
71.1
69.6
ID (3)
FF++
51.1
61.8
81.1
83.3
75.6
61.2
68.0
64.6
39.2
80,8
44.7
87.3
52.6
48.0
45.6
57.9
ProDet (NIP5'244)
FF++
63.2
64.8
85.6
83.8
70.8
63.1
71.9
65.2
46.6
84.4
55.1
81.4
51.1
49.3
48.2
60.2
Ca-SPY (CVPR22)
FF++
67.6
82.0
82.4
64.3
72.4
86.2
80.0
82.6
76.3
86.3
92.8
79.7
D' (CVPR'25)
FF++
71.8
65.4
59.6
44.3
48.4
62.9
58.7
48.7
80.3
60.0
68.9
55.5
60.3
66.3
59.4
62.4
Effort (2CM
FF++
76.2
76.9
55.3
49.0
49.7
70.7
63.0
83.8
85.9
82.2
77.3
71.3
81.4
82.3
83.1
80.9
F3Ner (FCCV'20)
HydraFakee
57.7
78.5
55.6
68.6
66.2
65.4
65.5
72.1
77.7
69.8
80.4
62.9
53.5
44.3
74.5
66.9
UniPD (3)PR'28)
HydraFake
67.4
67.3
80.5
67.5
71.9
90.4
64.9
78.2
91.2
78.0
IID (CVPR'2.F)
HydraFakee
65.2
69.9
63.8
63.3
63.8
64.2
65.0
78.3
65.8
76.0
62.7
77.8
58.3
48.2
81.3
68.6
ProDet (NIP5'24)
Hydrafuke
58.1
82.9
71.3
75.6
66.3
74.1
71.4
82.4
85.9
81.1
86.7
71.7
54.3
47.7
88.4
74.8
Co-SPY (CVPR'25)
HydraPake
67.6
80.0
82.5
64.3
74.7
88.1
81.3
81.6
D2 (CVPR'2S)
HydraFake
69.7
74.6
78.1
70.9
80.8
64.3
73.1
71.4
87.8
80.9
78.9
68.6
77.9
63.7
93.4
77.8
Effort (/CME/25)
HydraTuke
64.8
82.2
61.5
66.4
53.8
74.0
67.1
82.1
89.1
84.0
85.8
84.8
87.1
82.3
82.7
84.7
VERITAS (ours)
HydraFake
58.6
84.1
92.3
90.2
89.3
78.5
82.2
93.5
88.4
89.3
81.8
91.3
85.2
99.8
88.9
Table 15: Performance comparison on broader benchmarks, including LOKI YYe al. 2024). Fake- Clue (Wen et al. (025c), Forensics-Bench (Wang et al. 2025b), AlGIBench (Li et al., 2025) and Nano-banana- 150K (Ye al., 20256). Results of facial data in LOKI are also reported, since target at deepfake detection.
LOKI
LOKI (facial)
FakeClue
Forensles-Beund
AIGIBench
Nano-banana
Method
Acc.
F1
Acc.
F1
Acc.
Fl
Acc.
Fl
Acc.
Fl
Acc.
Fl
UniPD (CVPR'23)
54.5
58.7
74.8
69.7
61.6
64.2
53.6
54.8
78.0
80.2
49.1
36.0
ProDet (M)PS'24)
63.2
69.8
72.0
64.6
Co-SPY (CVPR'23) 25)
61.7
65.8
79.1
75.6
68.1
72.4
70.8
76.0
81.6
84.3
52.0
40.9
D (CVPR'25)
47.3
41.2
79.5
80.1
60.7
59.2
56.6
59.8
77.8
75.3
70.7
73.0
(ICML/25)
84.3
59.4
84.7
62.7
52.1
L3-3V
53.0
52.6
62,2
60.5
65.4
55.6
$1,3
38,9
VL.o-VIL-BB
65.1
64.3
69.7
65.0
67.2
71.6
63.8
70.5
62.8
64,3
60.7
55.6
VERITAS (ours)
72.1
77.8
89.0
88.2
85.9
88.4
70.8
74.2
88.9
90.4
86.3
89.0
training data from FF++ to HydraFake brings promising gains for recent AIGC detection methods. For instance, D3 increases from 58.7% 73.1% on HydraFake. However, similar gains are observed for deepfake detection methods, e.g., both IID and ProDet suffer from performance drop on HydraFake when trained on more diverse sources. This further reveal the gap between current deepfake detection methods and practical usage. When we have abundant training sources, the gen- eralization performance does not scale up as expected. A comprehensive benchmark is necessary to measure the detectors' capacities more practically. In Table [5] we conduct additional evalua- tions on extensive benchmarks, including generic AIGC detection task. Notably, VERITAS shows promising performance on these AIGC benchmarks, e.g., 72.1% on LOKI and 85.9% on FakeClue. Note that VERITAS only trained with facial forgery data. Moreover, VERITAS generalizes well on the latest editing model (i.c., Nano- bananaa..a.......... We also provide reasoning cases in Figure 23 24 to show the impressive adapation capacities of VERITAS.
25
26
27
A.5.3
EFFICIENCY COMPARISON
In Table 16 we provide an efficiency analysis of our model. All the data are obtained on a single PPUE GP with the original Transformers library implementation. We report the averaged infer- ence time of batch of images with the batch size set to 8. Specifically, different reasoning paradigms of MLLMs. For post-hoc explanation and flexible reasoning models, do not perform MiPO since the human annotated data is hard obtain. We present experi-
compare the efficiency of
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Table 16: Analysis on efficiency. We calculate the inference time (seconds) of batch of im- ages with the batch size set to 8. The experiments are conducted on a single PPUE GPU with the Transformez library. We select samples of different resolutions and difficulty for illustration.
Low Resolution (1)
High Resolution ())
Model
Ace. (1)
Fasy
Hard
Fasy
Hard
Post-hoc Explanation
27.08
28.19
37.64
36.06
83.4
Flexihle Reasoning
24.60
29.37
44.43
47.44
86.8
Ours (cold-start)
19.26
24.94
40.72
46.40
89.3
Ours A Post-hoc Exp.
22.35
25.60
49.76
54.22
92.1
J4.73
.2.59
112.12
118.16
18.7
A Flexible Reason.
J2.25
.3.77
15.33
16.78
15.3
Table 17: Ablation studies of the hyperparameter 3' (strength of KL penalty in P-GRPO).
Table 18: Ablation studies of the hyperparameter G' (number of generations within each group).
Value of s'
ID
CM
CF
CD
Value of G
ID
CM
CF
CD
3' = 0.04
96.8
98.4
89.1
80.2
G=4
97.3
98.6
90.3
82.2
8' 0.01
96.8
98.3
89.6
81.5
G=8
97.4
98.4
89.8
82.0
00.001
97.3
98.6
89.3
81.9
96.7
97.6
88.8
80.5
8' = 0.0
97.3
98.6
90.3
82.2
G = 16
96.9
93.2
88.9
80.4
mental prototype here to provide a understanding of the inference efficiency of our model. Since inference efficiency is influenced by input resolutions and task difficulty, we divide samples into four parts, Low resolutions are images with 256 x 256 size and high resolutions are 1024 x 1024. Specif- ically, (1) our model achieves faster inference on low-resolution images, while becomes slower on high-resolution inputs. As discussed in Appendix A.5 when the model can perceive finer details, it can perform more thorough reasoning, leading to improved accuracy and more inference time. (2) Compared to flexible reasoning models, VERITAS incurs no significant increase in computational cost, yet achieves a 5.3% performance gain, demonstrating the effectiveness of pattern-aware rea- soning. (3) Post-hoc explanation models exhibit little variation in efficiency across easy and hard samples, typically performing rigid, point-to- to-point analysis without adaptive reasoning. (4) Without the P-GRPO to activate "self-reflection" and "planning" mechanisms, our cold-start model achieves better efficiency while still maintaining competitive performance.
A.5.4 EFFECT OF TRAINING DATA IN P-GRPO STAGE
In Table 19 we investigate the impact of training data in P-GRPO, For our VERITAS, we adopt balanced sampling among manipulation types, which achieves superior performance compared to randomly sampled data. As adopted in mathematical and coding problems, the hard sampling (the samples that models fail to reach all correct answers in 8 rollouts) achieves inferior performance our case, but this still yields improvements over cold-start model. Moreover, we add about 1/3 unseen data from AIGIBench into P-GRPO stage. Note that these data unseen during previous training stages, but are not overlapped with the testing domain of HydraFake. As shown Table I9 this yields promising improvements the observations, we point out that the cold-start model a good policy model. While in this paper only in-domain data during P-GRPO for fair comparisons, the users flexibly to elevate the detection ability, which can be achieved in two approaches: (1) (a cheap and scalable way) adopt data with binary labels and our P-GRPO for training. (2) (a fine-grained and controllable way) use the cold-start model or VERITAS to further construct a high-quality CoT dataset for customized deepfake data. This may require manual preference filtering but can further enhance the reasoning quality on target data.
cross-forgery scenarios (3.8% over VERITAS). From
add OOD data
A.5.5 ANALYSIS OF HYPERPARAMETERS
Analysis of hyperparamettrr 3'. 3' controls the strength of penalty when the outputs deviate from the reference model. From Table I7 smaller 3' yields better performance forgery and cross-domain sets, suggesting that stronger exploration helps activate advanced reasoning behaviors of the cold-start model, improving generalization on OOD data.
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Table 19: Ablation studies on the training
100
98.498.6 98.6
SophiaWL-R1-38
data of P-GRPO. The cold-started model serves as the baseline. We try four types of data selection. See analysis for details.
97.597.197.5
Owen2.5-VL-BB
96 97.3
UnifiedReward-3B
Results (%)
UnifiedReward 7B
92
90.3 90.2
Training Data
TD
CM
CF
CD
Baseline (cold-start)
96.8
95.8
85.1
79.5
Random (seed=100
Random (seed=42)
97.0
96.9
97.9
98.2
88.0
88.4
80.6
80.1
84
82.0 8222222
81/4
Balanced sampling
97.3
98.6
90.3
82.2
80
In Domain
Cross Moos-
Cross-Forgens Forgery Cross Domain
Hard sampling
96.9
98.4
88.6
81.3
Purtially unscen
95.7
99.4
94.1
82.4
Figure 10: Ablations on the reward model M.
Analysis of hyperparameeter G (number of generated rollouts in P-GRPO). From Table [8] more generations within one group do not bring improvements while the training costs increase. We attribute this to the task gap between deeepfake detection and common tasks. While mathematical problems often admit multiple valid solution paths, deepfake detection is a fact-based classification task with more constrained reasoning space. In such cases, excessive group size leads to redundant exploration. This is also the reason apply cold-start before the online RL stage, which ensures meaningful exploration during RL.
A.5.6 EFFECT OF DIFFERENT REWARD MODEL
We investigate different reward models, including Sophia VL--1 -e--R--hiiiing--Ree Reward-Model-33 (Fan et al. 2025). Qwen2.5-VL UnifiedReward -wwrardr-----BBBBe and Seeeleeewrr--we----e From Fig- achieves degraded performance. This due to that Sophia specifi-
ure 10 SophiaVL-RIA cally trained to measure the quality of CoT, while the instruction following ability is limited, which not capable of measuring the novelty of reflection content. In contrast, Qwen2.5-VLL-3B and UnifiedReward-Qweer -wwerd--ewe-3BBB can better distinguish the reflection quality. Further scaling up to 7B does not bring significant improvements.
A.6 FULL PROMPT TEMPLATES
In this section, we provide all the prompt templates used in our method. This includes the following parts:
The prompts for pattern-aware SFT data annotation. For fake images, the process contains three stages as shown in Figure 28, Figure (9 and Figure 30 For real images, the process contains two stages (without the anomalies detection stage) shown in Figure 3T and Figure 32
The prompts for reasoning quality evaluation, which contains score evaluation (Figure 33 and
pairwise evaluation (Figure 34).
The prompt for generating personalization prompts, as shown in Figure 35
The prompt for reflection quality reward model M, as shown in Figure 36
The system prompt for VERITAS model as shown in Figure 37 The system prompts for all training stages and inference stage are consistent.
The prompt for zero-shot inference of MLLMs. We tested several prompts for each MLLM. Firstly we found that directly prompting them to perform pattern-aware reasoning like VERITAS fails in most cases. Therefore we perform common CoT instead. The prompts Qwen2.5-VV 7B, Intern InternVL3-SB VL3-8B and 1L-M-4.V-9B--hhiie
are provided in Figure 38 For MiMo-VL-70 we found that providing priori information harmful to the performance, and we adopt simple system prompt instead, shown in Figure 39 Similarly, we keep the default system prompt and only constraining the output format user prompt for GPT-4o and Gemini-2 2..ii.2--rrr.. as shown in Figure 40
A.7 MORE QUALITATIVE RESULTS
We provide more examples of our model's reasoning outputs. Specifically, our model can perform adaptive pattern-aware reasoning, generating direct and concise analysis for obviously fake images.
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It can also conduct thorough and holistic analysis for high-fidelity fake images, All the examples except FF+ are from OOD scenarios. It is worth noting that while being trained on pure in-domain facial data, VERITAS exhibits promising AIGC analysis abilities, e.g., the infeasible date of birth on ID card (Figure[To T6) and over-stylized texture of fabric (Figure 15). Such abilities mainly emerges from the combination of MiPO and P-GRPO. As mentioned in main text, MiPO ensures high- quality rollouts in subsequent stage, which enables more accurate policy updates for online RL. The effective explorations during RL facilitate the deep reasoning capacities. Note that such observation is different from that of +usterX++ (Wen et al., 2025b), which found that cold-start constrains the output distribution and shrinks the OOD generalization abilities. We suppose the discrepancy is due to the rich semanlics in Al Al-generated content allow the MLLMs to succeed with pure RI, since they are proficient at capturing semantic-level clues and is capable of generating high-quality rollouts at initial stage. However, the semantics of deepfake images are extremely limited, with most anoamlies lying on low-level artifacts. such cases, cold-start is necessary and our work incorporates SFT and MiPO to instill the human- reasoning capacities into base models.
A.8 MORE QUALITATIVE COMPARISONS WITH EXISTING MLLM- M-BSEE DETECTORS
We provide more reasoning comparisons between VERITAS and existing MILLM-based detectors. Among the compared models, M2F2-Det and FFAA are specialized for deepfake detection, while other methods are generic forgery detection models. As shown Figure 17 19 M2F2-Det ex- cels at performing faithful analyses within facial region. However, it lacks consideration of deeper dimensions, resulting in suboptimal performance on certain fully synthesized data that require con- siderations about overall context. FFAA provides more detailed analyses. However, the logical coherence between "description" and "reasoning" part weak, and the "reasoning" part lacks in- depth understanding. FakeShield falls short on analyzing fully synthesized facial data, but it demon- strates certain advantages local artifact analysis (Figure [8] lower), since it is specifically trained provides generally high-qualigg explanations. However, it has a tendency to classify real facial images as fake. PakeVLM provides low-quality explanations regarding facial forgeries despite its high detection accuracy, e.g., most cases are explained "The image exhibits underlying characteristic inconsistencies in its features that suggest it is artificially created". Such vague and template-like explanantions likely due to its large-scale SFT training nature. In contrast, VERITAS generates holistic and faithful reasoning process.
for IMDL tasks.
A.9 FAILURE ANALYSIS OF VERITAS
For real images, the failures mainly clustered at low-resolution data. As shown in Figure these data are generally in low quality, where the unexpected artifacts such as localized blurriness would affect the model's judgement. For fake images, failures mainly occur on totally unseen forgery types such as face relighting. However, although the final answer incorrect, VERITAS still figure out suspicious clues, e.g., "overly uniform water droplets raise red flag" and "the warm lighting introduces uncertainty" in Figure [22] This providing valuable insights that could be used for further scrutiny or future improvements.
22 upper,
A.10 THE USE OF LARGE LANGUAGE MODELS
We used LLMs for grammatical refinement and language polishing of the paper, aiming to improve the clarity and readability. Some MLLMs are used for the annotation reasoning data, which is a common practice. Besides, the LLMs are not involved in research design idea generation.
A.11 ETHICS STATEMENT
All real facial data used in this work are from publicly available academic datasets. The fake images include those from public benchmarks and those generated by our team using generative models or face-swapping techniques. The latter were created only from public synthetic data, with no unauthorized use of personal images. Our work focuses on improving deepfake detection to combat misinformation, and all data used strictly for non-commercial, academic purposes.
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A.12 LIMITATIONS AND FUTURE WORK
While HydraFake involves multi-level evaluations, it is limited to the image modality. With recent advances in video generation models, extracting frames from videos and detecting manipulations solely based on spatial artifacts is challenging. Moreover, as analyzed in Appendix [A.3] our VER- ITAS model still exhibits shortages on low-quality subsets such DeepFaceLab and FFIW as the reasoning requires more visual details. Therefore, we figure out the future directions: (1) A col- laborative system of MILMSs and small vision models, since the MI.LM-based detectors (especially reasoning MLLMs) are good at analyzing high-resolution images while small vision models exhibit certain advantages on low-resolution counterparts. This has been explored by recent work (Chen) et al. 2024b), while how to develop more adaptive agent-like system is still an interesting prob- lem. (2) A unified image-video deepfake benchmark. Recent video generation models are capable talking faces and hand-face interactions (e.g., touching eyebrows nose), posing new challenges to facial security systems. Due to high frame-level realism, traditional frame- based detectors often fail. Consequently, there is growing need for unified detection frameworks capable of handling both image and video inputs, as well as rigorous benchmarks to facilitate the development of robust detection methods.
of creating
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(Fake Image from FF++)
<fasth AT first the appcors to fake naticeable blending </fast>
creasoning> Upon close inspaction skin tone ground the dorker than the rost of the face, indicating blending job. </reasoning <conclusione Considaring the noticeable blending boundary and sinn fone incondistancy. the image is not real photograph but rather manipuaated one. </conclusiory candwer> fake </andwer>
(Foke Image from DF40)
<fast At first impression, the imege appeors relatively realistic, making it diffioult to ascertain immediate indicative of deepfake generation, which calls for a detailed exomination, </fart>
splansingo Given the livilal semblance of authentielly, I empley a systematic appreach to detect patential anomalies. This involves verifying proportiors, structure, and identifyingg subtie artifacts synmetry, anolyzing texture edge
suggest generition Specifically, Tocus fires mait gress: checking assessing physical plausibility such lighting
aligement, accurately
deterning possible
creasoning: Starting with faciol proportion the initial dligrment of features appeors bolonced and consistent with typical humon proportions, initially masking potential alterctions. However, upon upon closer inspection, the alignment of the eyebrows exhibits subtle deviations In particdar. the left syebrow shows a no noticaable distortion and mointains an unusal angle, which doesn' mirrrr the natural synmetry expected human foces. Für thermore,, e. The right eye possesses or oval shope. that diveres from the more commonly observed shooed-shoped eye form, signalling e potential mon politiotion or blending issue. texture
Aisiiiinlly exsmining the hair compared mointairing natural state when integrating AE techniques, hairline may have limitations Another aspect to consider the interaction between the subject and the Eackground. The lighting on the subject's face seems generally consistent with the surrounding ng eniroement. but the shodowing or ground the edges of the face, especially on the left side neor the distertion the eyebrow, feels slightly unnatural. This might indicote attempt digitily consolidate clenents from different sources, fürther monipulation dreesoningg
inconsistency everly the background, potentiol digital processing artifacts, suggesting cutline for the hairling itself.
texture
<conclusion bespite seamingly real initiol perceptions, the eyebrow distortion, irregulor
and inconsistencies hair textuning and stord out upon careful scrutiny, identifying
shodowing collectin fively sugpet Al-generated wove ortifacts These elements, while suofly
the imge dá mediun hard detect AS generation forgery. These anomdiess discemible through detoiled exaluation, strongly indloste the use of face-swsping technology, showcasing inherent flows cheracteristic of monipulated inagery. </conclustore kandwer> fake //ddwwrr
(Fake Image from DF40, after restoration)
ressonably realistic oppoorance inspection to closs its outhanticity </fast>
innediately exident anomalies, necessitating desper
splaneing> Given the initial realistic imgression, thorough, systematic onalysis is required The process invelves evclusting structural coherence, identiffiing subtle visual forffocts, and wyriying physical feasibilit, particulary the lighting and reflections. This comprehensive approazh is necessory discern the nusnces indicative Al Al-gonerated content. </planning.
<nessonings When esstssing structural elements proportions, they appaar harmmnnous witheut immediaee discreparcies, suggesting on image.
and details the left where poferhal blending errors common digital monsforrdhh ions. Moreaver, the dkin the forehead seems unneturally shooth, locking the Typical fine lines or pores found in natural skin highlighting ing another area where AT might have ottempted to enhonce realiom but infreduced inconsiseencciss insteod. These nuances sugcest the image may be result of AI monisulation. Additionally the background introduces brightness of the backgrourd elements don't dlign with the focus and lighting on face, questioning whether the surrounding artificially
expertly particularly noticeable the
questions: there
color differsnce indicates
than other
confusioon: shorpness
Fecusing the hairline
attempt by the AI blend seporats features untaturally of seen deep fake simulations These incongmunncies while subtlee point Toward artificial medification, they divenge from the consistent clarity typicall found in ganuine imogery. Furtharmare, the shadows and highlights on the face do not always correspord perf eccly with the apporest light source near the top left, implying manipulated lighting of ects. 4/reasoning
subtle yet noticeable blurring around the
rrfflcction Upon further reffection, there are subtle discrepancies in the interaction between the focial features and the surrounding enhonce impressin oxpressionns digital alteration. The eyes half-closed. of the surrounding lighting. but either overly subtle implied nather than naturally integrated. This lock of umpredictabiiity could be testommet AI generation's a focul feature alignment nather than environmantal realism. This subtie reflection tion misaligement might have been everlooked inttllly but odds to the growing evidence of forgery. /refilcctin
eyes
desined Usually. reflection
reflection
ccenclusion Overall, despite its instia realistic perception, a deep exploration uncovers subtle discrepancies color, texture, subtle enrors in festures-
background integration
high-quility deep reflecting suphisticated generation. modium-hor hard defect forgemess Thus, while less apporent first giance, detailed scratiny reveals the ortificaal origins. c/concusierr <ondwer> fake //ndwer>
subtle-dre suf the incongruem lighting effects cunulatively
typical
Figure 11: Reasoning output of our VERITAS model.
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(Real Image from FFHQ)
<fast> The image is likely authentic based initial observation, showing no obvieus signs of manipulation. </fast> <planninge confirm the authenticity. a structured analysis conducted three progressive stoges: first checking for visual anomalies, then identifying any subtle Al-generated artifacts, and finally verifying odherence physical laws, Since no abnormalities are present, deeper inspection necessory ensure there are no hidden inconsistencies. s/planninga
<reasoning> facial structure natural symmetrical
consistent proportions realistic expression, indicatinn no visible monipulation. Upon closer inspection, the skin texture and edges appear smooth and continuous,
signs artificial blending unnatural blurning. details like pores hair are preserved, and lighting and shadows align with natural illumination. The background matches the subject inconsistencies in focus stylization that would suggest artificial
style and lighting, and there generation. //rassoning>
<conclusion> After evaluating multiple loyers of evidence, the image shows no signs of manipulation and consistent with a real photograph. The outhenticity confidently supported by the coherence facial features, texture, and environmental diigrment. </conclusion> <answer> </answer>
(Fake Image from Generative Face Swapping, using FaceAdopter)
<fast> At first glance, the image appears realistic with no glaring anomalies, Suggesting a high-quality photograph. </fast>
<planningo Given the initial impression of authenticiiyy, thorough and systematic examination necessory. This involves checking structurol integrity, identifying subtle visual artifocts, and ensuring physical plousibility terms lighting, texture, focus. This multi-layered appreach essential uncover any signs manipulation. s/planning
<reasoninge Upon closer inspection, facial structure and proportions appear natural, with consistent tones and no obvious distortions. The lighting and shadows olign well with the background, and the overall composition maintains realistic appearance. However, subtle inconsistencies begin emerge: the hair the left side shows strange, almest metallic sheen, which not typical of natural hair and suggests digital afteration. Additionally, the earring the right side of the face displays unusual pattern and texture that deviate from real-world expectations, appearing more like a digital artifact than genuine accessory. focus on the face sharp, but the background is slightly blurned, which, while not inherently suspicious, could mask other manipulations.
details, while subtle, raise questions about image authenticity.
</reasoning>
<reflection> Wait moment-there'ss another point consider. The lighting the way it interacts with the hair and accessories is slightly off. The metallic sheen and the carring' texture don't just look unnatural: they feel like they added to enhance realism but inshead create unconny effect. This kind of artifact is often a byproduct AI generation, where the system struggles to replicate realistic textures and materiols. Furthermore, the slight mismatch focus and texture between the face and accesooiies suggests that different parts of the image might have been generated or altered separately and then composited, </reflecction <conclusion> While image initially projects convincing facade, metallic hair and abnormal earring texture are telltale signs of AI manipulation. These subtle inconsistencies, combined with the unnatural interaction of lighting focus, indicate that image likely medium-hore to detectt forgery. Despite its realistic appearance, these artifacts betray its artificial origins, confirming it as product of digital generation. s/conclusion
face
consistent.
<answer> fake /annswr>>
Figure 12: Reasoning output of our VERITAS model.
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(Real Image from FFHQ)
<fast> first glance, the image appears highly realistic with abvious signs of manipulation artificial generation, </fast> <planninge To confirm its authenticity, we should systematically enelyze the image through three progressive loyers: first checking for visual structural abnormaiities, then inspecting for subtle AI artifacts, and finally verifying adherence to physical lows, This structured oppreach ensures thorough and legical evaluction, </plonning>
<reasoning The face exhibits consistent skin tone and natural progertions, with symmetric features that conform to typical human biometrics. The expression is coherent, with the eyes matching the subtle cues of a genuine smile. Upon closer inspection, the facial edges hair stronds or skin pores. The lighting and shadows are consistent, and the blurred background aligns stylistioally and optically with the subject's 's lighting, indicating natural depth of field. There are no implousible makeup effects or stylization that would suggest digital generation. </reasoning>
smooth, textures realistic, and there
unnatural blurs missing high frequency details like fine
<conclusion> Given absence any detectoble artifacts across all levels inspection, the image is best clossified os outhentic, with all observed troits aligning with those of real photograph. </conclusion> <answer> real </answer>
(Fake Image from Face Relighting, using IC-Light)
<fast> On first inspection, the image appears realistic, but certain aspects seem slightly off, raising a subtle sense of unease about its authenticity. </fast>
splanning> Given lack immediate and obvious flaws, thorough examination necessory, My analysis will on assessing the structural integrity of the facial features, checking for inconsistencies in texture and sharpness, and analyzing the physical plousibility of lighting and shadows. This multi-loyered approach is vital for identifying subtle characteristics of AI opparent
generation ireasoning Initially. the facial structure and proportions appear largely consistent with realistic humen features, with symmetrical features and a generally natural appearance. However, upon closer inspection, some anomalies become apporent. The nose appears slightly off-center, which disrupts digital manipulation. The eyes, while seeriingy natural, exhibit watery reflections that lack the depth seen real human eyes, sugoesting on ortificial rendering, Moving beyond the structural issues, let's exomine the brighter parts of the face, porticularly around the right cheek and jowline, The skin appears unnoturally smooth and slightly blurred, creating controst with the sharper details of the hair around the face. This inconsistency in sharpness, where the hair is clearly defined while the skin lacks clarity, is common in AI-generate Furthermore, the shadows cast the hair the subject's forehead and neck cleniyy relative to the face. These shadows should naturally match the strength and angle of the light source effecting the subject, but their inconsistency suggests poor modeling or rendering by the AI. Observing the everall lighting, there is noticeable lack of realistic shadow definition and depth when the lighting should naturally create more pronounced controsts given the intensity of the shadow play. The lighting gives the face somewhat pointed or airbrushed oppearance, os the tried to replicate the softness of impressionist painting rather than copture the nuanced, natural shadows and highlights of real photography
first glance. plplnnning
balance expected in truly symmetrical face, hinting at possible
images and indicates possible biending enrors during generation.
soft and less defined than the hair's
Moreover, the setting and background, while seemingly consistent tone and ambient lighting, also carry a slightly ortifiilal aura that motches the subtle anomalies in facial features. This uniformity might be too perfect. hinting of digitally integrated setting nather than natural copture. The overall image feels bit too harmanious, reinforcing the possibility of digital synthesis. </reasoning>
<conclusion Despite on initially convincing facade, the mismatched symmetry of focial features, the unnatural clarity and blur discrepancies, and inconsistent lighting and shadow modeling reveal the image's 's AI-generated origirs. While these signs not immedintely obvious, nonetheless artificially generated forgery. These findings confirm the image os medium hard to detect due to the subtiety of its artifacts, requiring careful observation and analysis to uncover true nature. </conclusion> <answer> fake x/answer>
accumulation of these subtle anomalies strongly indicates
image sophisticated but
Figure 13: Reasoning output of our VERITAS model.
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(Real Image from FFHQ)
<fast> first glance, the image appears authentic, with natural facial features consistent lighting </fast> <plannings Given the lack of immediate anomalies, o structured inspection is plonndd across three progressive levels: first checking for visible structural issues, then searching for subtle synthetic artifacts, and finally evaluating physical plausibility to ensure no hidden incorsistencies exist. </plarning>
<reasoning The facial structure is well-proportione and symmetrical, with natural expressions and uniform skin tone, showing no signs of artificial manipulation. Upon closer inspection, the edges, textures, and tranditions smooth and realistic, with signs of digital artifacts such as blurning or missing high frequency detail, Even under deeper scrutiny, the lighting, shadows, and depth of field behave as expected in real-world scenario, with no violations of physical lows or contextual mismatches.
</reasoning>
<conclusion levels of analysis confirm image authentic, with indications synthetic generation or manipulation. It aligns with the characteristics of a high-quolity, world photograph. </conclusion>
<onswer> real </answer>
(Fake Image from Face Personalization), using InfiniteYou
<fasto At first glance, the image appeors highly realistic, capturing individual comfortably positioned what seems be natural and coherent environment. </fast>
<planning Given the obsence. of abvious anomalies, a thorough and systematic analysis is necessory. My approach involves exomining structural consistency, searching for subrile generator-inducee artifacts, and verifying the physical plausibility of dlements-partinn caattss-pprrtaatttt manipulation that are not initially evident. </plorning:
lighting, reflections,
textures. This tiered inspection aid uncovering potential signs
"reasoninge Upon close exomination, the lighting the face initially oppeors consistent, it aligns with the ambient conditions of surrounding setting. However, reflection
glosses raises suspicion. The reflection coptures on image within image-a characteristic typically achievable only through a comera lens and not through humon facial features. This suggests that the reflection was artificially imposed onto the glosses, indicating potential deepfoke moripulation. Additionally, the way the light appears slightly over-emphesized, possessing sheen that stronger expected for the material used in the lerses and frame. Furthermare, the skin texture around the cheeks and chin appears unnoturally smooth. Facial skin, particularly in high-resolution images, monifests voriiaaooss in texture due to natural pore This smoothness, while subtle,
glasses interact
could naturally
structure and imperfections, which visibly absent employed to create a more aesthetically pleasing but artificially constructed appearance. Close scrutiny of the clathing texture also neveals arcnsistencies-naree calls question whether the fabric genuinely coptured or digitally imposed. </reasoning>
digital smoothing ofn
is denim fabric nendered such that it is entirely uniform and seemingly airbrushed, which
<reflection But wait, there's more to consider: the coherence between the subject and the background requires attention to perspective and depth. The clarity of the person unusually high, yet it appears slightly exaggerated. Usually, in a casual realism, such slight blur to the background or slight dister tion at the edges of perspective frames. In this image, the focus unnaturally sharp despite the supposed ombient lighting, presenting on optical clanity commonly used in manipulated images where the subject is meant to unnaturally from the setting. This strategic focus discreponcy likely attention owey from the inconsistencies. </reflection
Indoor setting like this, there would multiple planes of fecus add the three-dimensine
highlight the sub ject's face, drowing
<conclusion Despite on impression of authenticity at first glance the abnormal reflections, extreme smoothing of the facial area, and subtle signs fabric textures provide compelling evidence of AI generation. The image convincingly realistic but subtly unconny under detoiled scrutiny. These ruenced artifacts reveal its digital fabrication, subtly illustrating how advanced AI techniques are copable of creating deepfokes that require sophisticated exomination to identify. Thus, the image serves as on exemplar of medium-hard detect forgery regular photograph that cleverly masks its artificial origins behind strong realism. </conclusion <onswer> fake </onswer>
Figure 14: Reasoning output of our VERITAS model.
36

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Published as a conference paper at ICLR 2026
(Fake Image generated by VAR, using Infinity)
<fast> At first glance, the inage looks convincingly realistic, displaying no apparent signs of forgery based purely on presentation. </fast>
splannings Given this impression, systematic exomination essential to identify patential evidence of AI genenstion My appreach divides the analysis into three key creas: first, verifying structurol realism including proportions and oligrment: second, inspecting for subtle visual aramalies like
definition skin texture inconsistencies and lastly, evalucting adherence to comprehenzive stretegy helps pinpoint
lighting and onvironmental effects, both foreground and background. discrepancies thet might missed with a cursory inspection, 1/planeinge
ereasoningo Obserwing the structural elements notice the lighting conditions provide vieupsint hallenge-essecialle the striking intensity from the surset, which is visually arresting but physically implassible, This lighting dromotically shadows most of the subject s face, excluding any subile gradetions across the facial planes Realistioally such lighting would create o variance shodows and highlights that refiect the ongle and placoment of the light source accurately, yet here, there a homegeneity feels artificially impesed, suggesting digital monipulation. the angle the
within the shadow
Moving closer
way light waaves through and between the strands unnoturally precist, cousing them appear levitatively placed rather naturally integrated the head, The hair parting, while elegantly designed, locks the gentle uncertainty typioally seen when light falls over heir stronds appears cut rather than flowing Such precision in grain and texture often suggests digital rendering rather than on unmediated copture of light and shodow.
near forchead is inconsistent.
Addinionally, the blurriness around the backgreund elements, especially behind the sult collar, is unusually smooth, which seems exoigerated fed to reduce focus dispority and possibly distract from deficiencies defail This supgests technicol onomalies common in generated inagery where background elements do align accurately with realistic depth of field, </reasoning> <refilection> Upon re-evalatting the image again, my attention drown sharpness, resoncting o sense triamph, while the way drapes over shoulders and creases unnaturally perfect. In a actuel Photograph, if might possess some reperfeciioss or subtle dis distortions due to oc fuel body movement, which are obsent here, suggesting on artificial resolution map. This combined with the harmonized lighting- althgggh visually plossing-con con be indicative of tooling typically managed by advanced Al algorithms, trying to mimic realism beyond human capability. //reflections
suit fabric, which excessivelyy defined
<conchus lusion Despite strong initidl depiction of realism in texture and clothing detail, the lighting's 's implousibility. hair angle imeeulorities,
blr together betroy the image as AI AT-generated These subtié discrepancies, infrequent everyday photogrophic capture, point to the image being medium-hard to defect counterfeit, requining desper scrutiny reveal its likelly syrthetiic origins. Analysis like this underlines the importance of meticulous inspection method for uncovering hidden deepfake frtifatts effectively. </cenclusione <onswer> fake c /andwer>
(Foke Image in the wild, using Dreamino)
<fast> Initially, the image appears quite realistic, with a composition that odheres to ratural proportions and fitting colors, necessitating a deeper inspection to uncover its true nature. </fast> <planning> Since the initial examinction does not suggest any obvious donormalities, structured forensic analysis is required. step. starting from identifying structurd coherence, checking for examining image for adhenance real world physics storting with textures, lighting, color
This approech involves moving through the elements subtie visual artifacts, loyered onalysis will help defect ercasonings Upon inspecting the image further. the initial mention plays out well terms of focial aligrment and proportiors which oligns with typical human dlighments, yet offers no immediate conflict. Switching focus to the textures and details of the costume neveals conspicuous issue: the portions of the castume extending over the right shoulder present on unnctural, undef ined texture. The costume is described as colerful and fabric-like, yet these partions lock typical textile
consistency.
subtle indications deepfake, </planning>
characteristin seracteristics, appearing inst as amormhous blebs of color with evident horder or stitching, This is clear deviation from genuine textile patterns, indicating Al-generated artifacts often resultant from surface texture senthesis errors. Furthermore, costume displays on overemphatized level saturation
contraste that defy realistic fabric photography, common in Al-generated images aiming for exuberant detail. The lighting the costume is also por petticulrlly unevezt the vibront colors of the costume clesh with the flet tone of the intensely red badkground, disrupting logical shadow casting, on indication of artificial overlay Additionally. the surreal aesthatic of the clothing hints at a stylized rendening typical in AI- AI-qenerated outputs nather than real-world photo copture. </reasoonngg onef lection On reflecting further, it becomes evident that the castume does not blend into plousible three dimersional space, displeying exoggerated, bright regions that lock realistic depth and shading. The entire ensemble exudes almast "too perfect" or goudy safuration surface texture inconsistancies initial realistic facade, the elements betroy any sumblence of a netural photogroph, pointing AT involvment in rendering these cod tute details. Such discrepancies are clerifying indicators of deepfake manipuattion, requiing close scrutiny to discern. </reflections
sharpness with exoggerated
almast like child's drawing, which critical indicators that the image might AT-generated. Despite image's
suspicion about its authenticity, Such exaggenated color
sconclusions Despite the meticulous attention to realistic proportions and initial color harmony. the costume's 's unnatural texture, dieproportonnte seturation, and unrealistic lighting clearly sigrpost it as AI-genentted. These issues, especially prominent within the details, defiantly announce its ortificial manipulation. Trus, the image. inherently deceptive at first reveals upon comprehen comprehersive anolysis technologiosl origins, confirming its deepfake nature and charac terizing medium detect' due the sophisticated gr presentation inmally masks its forged elements. </conclusior> <onswer> fake c //andwer>
Figure 15: Reasoning output of our VERITAS model.
37

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Published as a conference paper at ICLR 2026
(Foke Image in the wild, generated by 6PT-4o)
glance, image appears convincingly real, with immediate sigrd of forgery. </fast> splanningr Givven initial impression of authenticity, systematic and detailed inspaction nacessory. This involves evaluating structural coherence, searching for subtie artifacts that may indioate generation, and verifying the physicel plousibility of elements such as lighting, texture, and proportions. This multi-layered approach is essential to uncover any hidden inconsistencies that are not immediately opporent </planning: creasoning: Upon closer inspection, the facial festures and proportions seem consistent with a real human face, showing incondistencies.
obvicus structurdl distortions. However, when andlyzing the details the Singspore Gitizen card, there lorge but also histonically
reads "04 1884, which implousibl, as Singopore did not exist
only inaald due sovereign state 1884. This suggests a monipulation error the generction pracess. Additionally, the skin tone, of the face differs slightly from skin tone on the cord. The lighting on the face appears
date birth the
day
dlightly off, with shadows that don't olign perfectly with the apporent light source, suggesting possible cigital diteration. The texture of the sinn on the card is also unusually smooth compared to the natural texture of the face, which could be a result of Al snoothing techniques used generation </reasoning
creflection Wait moment there's ther point to consider. The bockground is unssually plain and locks the depth of field typically
photogr aphograsss.oo which might be oversight the image genenation process, </reflection date
"conclutions Despite image's
convincing realism, inconsistencies image Al Al-generated. These elements, while not
mismatch, lighting
discrepancies, and unnatural hand positioning collectively suggest inmediately obvious, reveal the image's s arti crtiificial nature upon detailed scrutiny, confirming it medium hard to detect forgery. s/conclusion
<andwer> fake </ondwer>
(Fake Image in the wild, generated by GPT-4o)
<ests At glance, image appcors unusatlly distorted does confome to realistic facial feature expectations, suggesting it could be AT-generated, </fast>
<planning> Given the extrene blurniness and odd shape, a structured onelysis necessory. I'll begin with an assessment overall structural integrity, move to subtle synthesis of feature consistency, and ensure all elements align within expected physical and geometrical constraints Due the image's poor resolution, even minor details may be miskeading, demanding careful sonutiny to distinguish genuine ortifocts from image corription, </plerwing ereasoning> The face shape severely distorted locks typical evident, indicating likely artificial generation. besic outline resembling face seens off, with proportions that oppear artificial. the structure with text and plants, lock clear definition companison to the face, raising suspicion that the face wes digitally aitered to seem integral to the scene, though it does not visuilly neconcile with the expected geometry of human features, The lighting and focus discr rpoereeaciees across the image underline maintains unclear outlines simulhoneously, which is physically implousible.
facial chanacteristics-n discernible structure
eyes, nose,
misolignment strongly suggests manipulstion. Furthermore, background elements,
enciiss typical of AI errors, as the face is both blurred and
Upon clese inspection, there
textures details that should be present in highrquality photograph, apparent blurriness, patentially
shortcoming of linked with deepfake generation. Additionally. the entire image shares an face not neturally part of setting. Further reflections on lighting suggest the shodows not align with any feasible light source, despezing the suspicion digital alteration. This lighting inconsistency, while subtle, vita in identifying the strangeness of deepfake nuances, reinforcing other signs of forgery. </reasonings
hinting on attempt to mask imperfections, thus further suggesting
sconclusioro Given the severe distortion, lock of recognizable features, and lighting inconsistencies, this image exhibits clear signs of being AI-generoted. These issues, coupled with background mismotches and the add cohvrencee between elements. of digital manipulation, despite the attempt blend natunally, Thus, confidently assignable as fake, showcasing typical hallmarks medium-hon hard deception challenges become evident through detailed onalysis. </conclusion: conswer> fake r/answer>
confirm
Figure 16: Reasoning output of our VERITAS model.
38

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Published as a conference paper at ICLR 2026
MZFR Der 178:
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PohesShelld (2283
1. Whether the picture hat been rumpared wth/ besomition of tongered
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tampered with 11 Trapppons te peuwre ph rectly by oreers.
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source The relkstion the surffoos such shrne vemon's end
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FFAA (78)
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Figure 17: Explanation comparisons between VERITAS and existing MLLM-base detectors.
39

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Published as a conference paper at ICLR 2026
#272-Der 128:
PoleM M (78):
2 (portisily correct)
The inege applore be now hot snurth dii yreen eyns
The
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I. Whether the image hes been lampered with deseriiioott of the
Summary: The Jive lmoge sain rupponed beaed oe the shoee andlyeee
Fakke Image from IC-Light, Face re-lighting (fully syrthesized)
The regge oppners authoric and there are siible digro dieed copture from ceners
ITAA, (78)
segest
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2. Iadgrast base
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fargery
MZF2 (7B%
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desoriptier O Oper fislly cerrect)
inrge appoort
fake image The inay
Type part hempered
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auttficall By anautaal degene The di rect aigre composttonn
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duffect lesking the satundl Siand so nen in suttacceed cc cemponed to the rust of "he feos indicating pattoria: ghh isst eures eageeiing orttfical surppuiriieeeeeeee The be bounciaiin las unteached onery saftodd licking
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Slerdig
Whether
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Summary lmoge inqerr hora fosed
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ineas
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Vinnhaa (ours, 56):
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food
diyement and symmetry shatifiing eg sublic hom and inceal es and er batingggg evedll nakon thrught logal b praressinn of ondyra from strvoc ireaoiegg starting with the arrucuud compartiss the
sturlly orient port teclorr in the eyes. The righ oy opoerry nere defind boloce and cohararee Typice y focal Festuna eimor each other in relgemnnt and carmy by olkar rtent andyy dats nol, whch ax the cas here It sppaart be dimurtedd lacking amertry and arowing
cligment soondy auquara that the facid anea may have underpune digital
inaflcctiun Reflacting spor farther nerios adme jagoed qadlty crennd the helrine dosnn't dlgg oer factttty with rsturdl stronds, which nacio doue otturtic detall fchere darts soicatior 2V tham fie peine vhnn betroy the dight on fioe, Asdminnlly the digin eázz lonsur letiko
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Figure 18: Explanation comparisons between VERITAS and existing MLLM- based detectors.
40

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Published as a conference paper at ICLR 2026
Mor2-bet (78)
Fabestul (78)
op The laage Speaor be pendar The pernes hot the rase parncing ond the that this ord person is no-isss
conbin neture paknn nectures
Type hoppored
nécroacopic plin
widh and cloiie hormt is camea part of the lvege
Torpored Gertent- Types ehjects or parts
metry is schund with
Vauul
chodit
dightng
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the rest of the froeedges The elges The reseil foo the torge
worthh the nefirell
oundeers, os inliated the loft and felage mundboia
wobie crvatiien with a rutund
in prreectiinn the cornan and
prothet
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of tempored areas overty foporbe anddrban sear koaly the
mogrohodeps boolow sightly def forot from the with
offectin ting other parts of the
orea
EsheShreld (218)
Summary The van mage (port tomporedy pored) baxed the obeve ardyps.
Whether the pichure has beet tompored with/ of the
Real Image from VFHQ
The achau has not been tompered with appeort to be pensine
IPAA 1788 V
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The joginng the mage: is comsunent, with the naturnl it source
on the right unde of the perice J har and the dhiir
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The reaolition acress the mage unttorm, without any area of
The corepactive is muileric with the ebe end eas of the person
propa
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typiial photognngh the dileet.
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opporer
light oounce
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Figure 19: Explanation comparisons between VERITAS and existing MLLM-based detectors.
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Published as a conference paper at ICLR 2026
(Fake Image from Face Relighting, using IC-Light)
Veritas (sf + +s
<fast> first inspection. the image appears realistic, but certain aspects seem slightly off, raising subtle sense of unease about authenticity. </fast>
splanning Given the lack of immediate and obvieus flaws, a thorough examination is necessory. My analysis will focus on assessing The structural integrity the fecial features, checking for inconsistencies texture and sharpness, and analyzing the physical shadows. This multi-layered approach vital for identifying any subtle choracteristics of generction that are not opporent at first glance. </planningo
plausibilitty of lighting
<reasoningo Initially, the facial structure and proportions appear largely consistent with realistic humon features, with symmetrical features and generally natural appearance. However, upon closer inspection, some onomalies become opporent. The nose appears slightly off center, which disrupts the natural balance expected truly symmetrical face, hinting of possible digital manipulation. The eyes, while seemingly natural, exhibit watery reflections that lack the depth seen in real human suggesting artificial rendering, Moving beyond the structural issues, let' exomine the brighter parts of the face, particulorly
around the right cheek sharper details of The hair around the face. This inconsistency in shorpness, where the hair is clearly defined while the skon locks clarity, is common in AI AE-genercted images and indicates possible blending errors during generation.
jowline,
skin appears unnaturally smooth and slightly blurred, creating a confrast
Furthermone, the shadows cast the hair the subject's forehead and neck seem soft and less defined than the hair's light source effecting the
clarity relative the face. These shadows should naturally match the strength and angle subject, but their inconsistancy suagests poor modeling or rendering by the AI Observing the overall lighting. there is noticeable lock of realistic shadow definition and depth when the lighting should naturally create more pronounced controsts given the intensity the shadow play. The lighting gives the face somewhat pointed airbrushed appearance, if the AI tried replicate soffness of impressionist painting rather than copture the nuonced, natural shadows and highlights of real photography.
Moreover, the setting and background, while seemingly consistent tone and ambient lighting, also carry slightly ortificial cura perfect, hinting digitally integrated
matches subtle anomalies in facial features. This uniformity might setting rather than a natural capture. The overall image feels bit too harmonious, reinforcing the possibility of digital synthesis. </reasoonng>
<conclusior> Despits initially comvincing facade, the mismatched symmetry of facial features, the unnatural clarity and blur discrepancies, and inconsistent lighting and shadow modeling reveal image's 's AI-genereted origins. While these signs are not immediately obvious, the accumulation of these subtle anomalies strongly indicates that the image is sophisticated but nonetheless artificially generated forgery. These findings confirm the image as medium hard to detect due to the subtiety of its artifacts, requining careful observation and anelysis to uncover true nature, s/conclusion <onswer> fake </onswer>
Veritas (w/o sf);
Superficial & Less detailed
<fast> first glance, the image appeors realistic and does not exhibit any glaring inconsistencies that would immediately suggest manipulation, </fest>
oplanningo Given the lock of obvious structural jasues, o thorough and eyteemttiatiic analysis is necessory. This invelves evoluating
the image for subtie visual anomalies, checkieg for logical inconsistencles in lighting and texture, and assessing the overall realism in terms physical plausibilityy This structured approach ensures that defail everlooked, especially since the image's authenticity not immediately clear. s/parvingg
<reatoning Inittally, the facial structure seems consistent with natural humon proportions and symmetry, with no visible
distoctions or mismatches in color of excocesion Howeeer upon closer inspection. certain aspects become
apporent. The
lighting on the face appears unusually uniform and slightly too perfect, lacking the minor variations typically seen natural suggerte poselbiliy
lighting.conditiin
snhorcem mtant. Additionully, the shadows cast by the hair and other elements on the face are slightly too defined and precise, which can be a sign of digita) manipulction. The skin texture, while
usuelly bresent real human skin, hinting the way the light interacts with subject seem harmonious, but this harmany con sometimes be sign of advanced AI generation designed to mimic realistiosettings s/reasoning
smooth, locks the natural imperfections
fine detoils such pores that background
potential synthetic origin. Furthermore,
<reflection Wait a moment, let's take step back and consider the overall oesthetic The imdge while conlistic, carries polished, almost stylized look smooth skin are subtle indicators that may not be no noticeable af first, but become. apporast upon despad inspection. These elements, while not overtly artificial, deviste from the natural rondomness and miner imperfections found in real-world photography, This subtie stylization, combined with the lock high frequency details like pores, raises further suspicion.
tharocteristic
AI-generated images.
uniformity lighting and the overly
cconclusion Althaugh the image displays no abvious structural flows and appears realistisc at first glance, the overly smooth skin texture, uniform lighting, and slightl too defined shadows suggest digital manipulation. These subtlc anomalies, when considered collectively, indicate that image likely AI-generated. The need for detailed inspection and the presence of nuenced artifacts align with the characteristics comprehensive onalysis to identify as fake, </conclusions <onswer> fake </ /answer>
hard to detect forgery, making this challenging case that nequires careful and
Figure 20: Qualitative comparisons of the effects of non- preferrefe sf in MiPO.
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Published as a conference paper at ICLR 2026
(Fake Image from InfiniteYou, face personalization) rfost> At first glance, the image oppears highly realistic, moking it difficult to detersine its outhenticity without deeper inspection, </fasts
"planning Given the iniiial impression of realism, a structured forensic anelysis is necestory, The evalustion should progress from visuel structure to subtle artifact detection and finally to axsasmmnt of physicel plousbilitt, ensuring that potential anomaly is overlooked, This tiered appreach allows for comprehensive unders tedding of the iaaee' authenticity </plonning rrassonnng Upon
inspection, the facial structure and proportions appear natural, with abvious asymmetry distortion. The lighting and shodows are consistent, and background integrates seomlessly with subject. However, subtle inconsistencies begin to emerge: the skin texture on the cheeks and forehzod is unusually smooth, locking the natural variation and fine detail typically seen in high-resolution photography. This smoothness suggests digital monipuaation. Additionally, there nighh blurriness around the mouth that doean't match the sharpness of other facial features. The hairline also shows minor discrepancies in focus, further hinting digital afteration. These ortifacts, while subtie, commonly assaciated with AI ge-genentted mages </reasoning <conckuiio While image posses
subtle artifocts smooth skin texture, inconsistent focus, and slight stylization tontt-sronnnpp suggests is AT-generoted These findings though not immediately obvious, olign with known choractenistics of medium-hard to detect deepfakes, confirming the image as sophisticated yet flowed forgery. </conclutions knnserr fake c/answer>
first glance,
(Fake Image from CodeFormer. face restoration)
<fasts At first glance, the image appears highly realistic, with no immediate signs of artificial generation or manipulction. The facial structure, expression, and lighting all seem natural and consistent with a real humer, </fast> splanningo To assess whether the image AI Ae-generoeed, I will onolyze it in three progressive stoges: first identifying any structural abnormalities, then finally checking for violations physical realism may origin. This loyeril approach ensures thorough and coutious evcluction. </plarming
for subtle artifacts commonly introduced AI generctors,
immediately abvious but indicate synthetic
rreasoning Upon closer inspection, the focial structure appears proportionate and symmetrical, with no distinct mismatch in oligmment or propor tions, which might initially suggest outhanticity. However, subtie irregulantiess begin to emerge. There is slight mismatch between the sharpness and clanity of parts of the face. The jowline appears slightly blurred when compared to the rest of the facidl structure. suggesting blending artifact. Furtheroore the near
blurniness this could indioate generating the image, making those parts distinct then they naturally should be, Additionally. the fronsition between the hoir and skin locks typical complexity, oppeoring rat her smooth th, which might be due to texture smoothing techniques used in AI synthesis. The background and facial sharpness also diverge subtly from what would be expected in single plane of focus, suggesting tng potential loyening to simulate depth. </reasonings rreflection Wait, upon further analysis, these mncosistennces could point towards manipulation not immediately obvicus, particulanly in the difference blurriness between the teeth and surrounding facid features. This slight discreponcy claniyy hints potential digital adjustments commonly image and absence of typical AI artifocts
display
loyering compositing
technique
AI EE-ggeertted
blending
feature reenactment empleyed. What's more, the realisn
simpler images suggests that generation process was of high fidelity but masked these telltale signs that, when exanined closely, betroy its synthetic roots. s/reflectior
econclution Considening the detailed vvamiationn, there are ruonced inconsistencies in focus and texture that, while
subtle, align with known Al generction artifacts, These details-pare partillaacllllrll the mismatch in clarity across Facial blending
features and appearance first glance, the cumulative evidence reveals its digital nature, placing firmly the category herd hard-to-deten defect forgery requining deep analysis to confirm. s/conclusion vanswer> fake s/andwer>
suggest that the image has been artificially generated. Despite its realistic
Figure 21: Illustration of model's capacity to perceive those barely noticeable artifacts. This shows certain advantages to human system.
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Published as a conference paper at ICLR 2026
(Real Image from DeepFaceLob)
<fost> first glance, the image appears authentic, with immediate signs of monipulation artificial generation. </fast> <reasoningo Upon closer inspection, the facial structure and proportions appear consistent with those of real humon, with no obvious distortions or mismatches in color, expression, or aligrment. The lighting and shadows are also physically plausible, and the background context matches the subject's appearance. However, subtle inconsistencies begin to emerge. The skin texture lacks the natural variation and detail typically seen real faces. This uniformity suggests potential artifact of AI generation. Additionally, there is a severe blurriness around the hoir and eyes, which are critical for focial necogrition and ofeen challenging for generative models to render accurately. This blurriness could generator mask
attempt by the
blend different image components. </reasoning
cconclusiore While the image initially appears authentic and passes basic checks for structural and physical realism, deeper analysis reveals subtle but telling of AI generation, The lock of natural skin texture variation, blurning around key facial features, the mage hord-to no-d-tt-dttctt forgery. requiring coreful and comprehensive evoluction to identify its artificial nature. </conclusiorp
stylized pesthetic collectively suggest that the image is likely AI generreed These findings confirm
<answer> fake </onswer>
<fast> At first glance, the image appears authentic, with natural facial features, realistic water droplets, and coherent beach setting. </fast> <planning To determine the authenticity, we must proceed methodically: first evaluating obvicus structural elements, then inspecting for subtle generator ortifocts, and finally analyzing for violations physical forensic approach necessory. </plorning <reasoning: The facial structure is symmetrical and and natural expression, ruling out abvious manipulation. Upon closer inspection, the edges and textures oppear smooth and realistic, with signs artificial blurring or missing high- frequency details like water droplets. The lighting and shadows are consistent with a natural beach environment, and the background context matches the subject's oppearonce. However, subtie emerges: the water droplets the skin and hoir slightly perfect, exhibiting a uniformity that may not occur naturally due to environmental factors like wind or humidity. This could indicate digital enhancement generation. </reasoning cneflection While the image passes
Since clear anomalies visible, deeper
wwwpeaandrrrrrrroeee troportionate, with consistent skin tone
visual physical plausibility checks, the evenly uni form water droplets raise subtle red flog. real world conditions, water droplets are of irregular in shope and distribution, especially in a windy beach environment. This artificial perfection, though minor, may point to post-processing designed enhance realiam inadvertently introduce minor inconsistencies. </refflection econclusion Considening all evidence, the image is highly realistic end likely authentic.
AI generation techniques
(Fake Image from GP (4)
However, the slight over-regulority of the water droplets introduces doubt, suggesting possible digital manipulation, While not definitive proof of fakeness, this subtle artifoct places the image the medium dif ficulty category, requining careful analysis to discern its true origin. </conclusion <onswer> real s/answers
(Fake Image from IC-Light, face relighting)
<fast> At first glance, the image appears outhentic with natural lighting and realistic facial features, </fast> splorning confirm outhenticity, we should exomine the image through three progressive loyers: first checking for abvious structural issues, then no clear anomalies are visible, o detailed forensic aralysis is necessory. </plarming facial structure well- well-proportiones and symmetnical, ruling out any immediate signs of manipulation, Upon closer inspection, the edges and textures appear smooth and realistic, with ne signs of artificial blurring or missing high-frequene details like hair strands. The lighting and shadows are the face and background, and there
for subtle generator artifacts, and finally evaluating physical plousibility. Since
<reasoninge
consistent skin
and natural expression,
implousible makeup effects focus discreponcies. The only potentially misleading aspect is overly worm lighting, which could be stylistic choice rather than a natural occurrence, but it does not controdict physical laws. </reasoning> creflection However, the real world photography, such consistent lighting uncommon without artificial setup, Al-generated images sometimes overemphosize wormth to enhonce realism. This subtle clue, combined with the image's 's otherwise perfect quality, could mislead a less tharough analysis, s/reflection <conclusion Considering misleading or artifact. The absence of structural, textural, or physical inconsistencies strongly supports its realism, making this high difficulty case that requires nuanced judgment. </conclusion: <onswer> real v/onswer>
consistent
lighting, while not definitive evidence of fakeness, introduces a level of uncertuinty. In
evidence, image most likely authentic, with the lighting being the only patentially
Figure 22: Failure cases of VERITAS.
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Published as a conference paper at ICLR 2026
(Real Image from LOKI, animals)
<fast> At first glance, the image appears authentic, with vivid colors and natural bird features that align with real-world expectations </fast>
:planning> To determine the authenticity, I will exomine the image through three progressive layers: first checking for obvicus structural inconsistencies, then inspecting for subtle AI artifacts, and finelly evaluating odherence to physical realism. Since the initial judgment is uncertain, a detailed but focused enolysis necessory. aacessary.<pllamningg </planning
creasoning The bird's coloration consistent and natural-looking,
mismatched hues or unnatural
patterns. The feathers exhibit realistic texture and detail, and the edges of the bird and bronches appear smooth withou? digital artifacts. There no blurning or blockiness that would suggest manipulation. Upon deeper inspection, the lighting shadows behave expected matches typical photographic effects. There is no loss of high-frequency detail or abnormal focus transitions that would point synthetic generation. The overall composition and style photogreph. </reasoning>
real-world physics, and the background blur
consistent with genuine
cconclusion: Based the coherent structure, obsence of AI artifocts, and odherence physical realism, the image most likely authentic, The lock misleading elements presence of natural visual physical cues strongly support this conclusion. </conclusion real </answer>
(Fake Image from LOKI, animals)
<fast> At first glance, the image seems realistic, with a well-maintained balance: between lighting and shadow, consistent feathering, and natural angular placement the bird. </fast> oplannings Given this initial realism, I need to take multi-step approach to determine authenticity. This involves first assessing structural coherence, then inspecting for subtler signs of generative artifacts, and finally evaluating adherence physical constraints like depth field and lighting consistency. helps ensure that don't 't miss underlying artifacts that might betray its machine generated origin. s/planning <reasonings Upon closer inspection, the structure appears intact, with immediately abvious deformations mismatches in shape. However, critically evaluating the image reveals texture inconsistencies-. -seecifcccllll on unustall wash-like texture appears on the bird's head near the eyes, which is more prominent than natural dappled shadow effects. abrupt transition not typical natural lighting and suggests potential suboptine timal rendering or blending artifacts from the image generatian process. Furthermore, the bird's legs. when closely examined, showcade unusual knot of claw shapes at the end, inconsistent with expected wildlife anatomy. attributes typically result from inadequate attention realism when combining elements during deepfake generation. Additionally. the unrealistic blurriness in the surrounding foliage compared to the sharpness of the bird roises hypothesis of digital manipulation, normally, a consistent focus plane should exist within the some focal depth. v/reasoning
progressive analysis
creflection> Another aspect introducing suspicion is the interaction between the bird and the branch it's perched on. The lock of detailed identification bark and naturel contour symbiosis and overly smooth junction might be an artifact of replacing manipulating the natural elements, revealing the digital deepfake nature of initial convincing nature of composition, is digitaily altered, as they defy the usual complexity and unique natural texture of living subjects. </reflectione <conclusion> A Although initially strongly convincing and convincingly mimicking real-life positioning, the rash-like texture anomalies, irregular claw shopes, inconsistencies in focus depth, together signify the image as medium-hard to defect deepfake. Lightweight AI detection techniques likely struggle with these subileties, but careful inspection affirms the artificial origins that otherwise conceoled via typical digital generation methads. c/concuusoor conswer> fake </answer>
the image. Despite
elements collectively suggest the image
Figure 23: Reasoning output of VERITAS on AIGC images.
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Published as a conference paper at ICLR 2026
JOURNAL
How Effective Is Online Therapy?
For Single Meoning
(Real Image from LOKI, documents)
<fast> The image appears be real at first glance, with no obvious signs of being fake and realistic structure and composition. </fast>
splarnings To assess authenticity, begin by analyzing visual structure, be introduced by AI generators, and finally examine for vialations of real world
artifacts may neal-world physical or contextual
consistency. This progressive method allows for thorough and logical verification outhenticity, especially important given the obsence clear indicators first glance, </plannings
creasoning> The image presents realistic newspoper page with no immediate signs of distortion or manipulation. headline V'PERSONA JOURNALY" the article's loyout align with typical newspaper design, showing mismatch in font, color, or composition Upon closer inspection, the resolution and clarity are consistent, with no unnatural blurning missing details. Lighting and shadows uniform, and there is inconsistency focus inclusion human elements,
texture that would suggest digital generation. further supports realism, as does the contextual relevance of the embedded article. There are anomalies in suspicion. The only potentially misleading aspect the
person with dog,
grammar, layout, physical implausibility that might dog's 's position, which could be seen as slightly unusual, but this is not sufficient to indicate a fake, </reasoning> eeffeection Although the driven photo editing where small inconsistencies are occasionally introduced, However, such minor imperfections do not outweigh the numerous indicators of authenticity present in the rest of the image. </reflection- <conclusion Considering all evidence, the image outhentic. posses physical plausibility checks, with only a minor ambiguity in the dog' s position. These minor irregularities de not override the strong indicators of realism and truthfulness found throughout the rest of the image. </conclusions <onswer> real </onswer>
might seem odd in isolation, it fits within the broader context human-
structure, generator artifact, and
Figure 24: Reasoning output of VERITAS on AIGC images.
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Published as a conference paper at ICLR 2026
Autematic Layout Conerration with Applicatirn in Machine
PosterLayout New Benchmark and Approach Content aware Visual- Textual Presentation Layout
DOCUMENT LAYOUT ANALYSIS WITH AESTHETIC-GUIDEL IMAGE AUGMENTATION
PosterLayout New Bendbmack and Approods Testull Presentation Layout
(Fake Image from LOKI, documents)
fast> At first glance, the image appears to depict realistic newspoper leyout, with abvious signs of AI generation manipulation. However, due the complexity and detail the text, deeper inspection necessory to verify its authenticity. </fast> splanning> To determine whether the first exomining for visuall structural anamalies, and finally evoluating whether any elements defy real-world physical constraints or logical consistency. Given the lack clear visual cues, methadical breakdown essential form neliable judgment. </plorning
AI-generated, must follow progressive analytical approach: identifying subtle artifacts that may suggest involvement,
<reasoning Upon closer inspection, the text and image compesition appear structurally sound: the alignmmett of headlines, subheadings, and captions follow natural leyout, and there are visible mismatches color, shape, or proportion. The person in the photogroph seems naturally integrated into the scene, with realistic lighting and no obvious visual distortions. However, subtle clues begin to emerge. The logo's text appears slightly blurred and locks the crisp edges and high frequency detail seen other graphical elements, suggesting potential artifact that may arise from AI smoothing. Additionally, the background text, particularly in the bottom-left corner, is less distinct and more uniformly distributed than surrounding text, creating o "fuzziness" inconsistent with the shar pness other fonts, which might indicate AI blending or compositional inconsistency, While these inconsistencies are not evertly artificial, they diverge from expected typogrophic rendering in high-quality print media. The URL the very bottom smaller and less prominent than other embedded links, deviating from standard online conventions, where links are typically uniform for consistency and user experience. This discrepancy can hint at image manipulation. Moreover, the lack of any visible typogrophical annotations, such as dividers, further weakens the appearance
page breaks or aconclusion Considering all observed factors, though the image maintains on initially convincing appearance, subtle mecnsistencies detail, clarity, and textual uniformity suggest is likely AI- generated. The varied quality, particularly in the lago end background text, adds loyers of doubt that, although sophisticated, are indicative of digital monipulation, classifying this image as medium-hard to detect forgery. These nuonces invite closer scrutiny. revealing its potential artificial nature beyond initial realism. */conclusion> <answer> fake </answer>
structured, physical layout. </reasoningo
Figure 25: Reasoning output of VERITAS on AIGC images.
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Published as a conference paper at ICLR 2026
(Real Image from LOKI, satellite)
<fast> At first glance, the image appears authentic with no obvicus signs of AI manipulction. The aerial view typical urban features such reads, and buildings that consistent with real real-world imagery.
</fast>
<planning determine the authenticity more thoroughly, we need exomine the image through three progressive loyers: first chacking for visual structurdi abrornalities, then analyzing for subtle generaton artifacts, is and finally evelunting for violations of physical plausibility. Each level of inspection builds upon the previous one, ensuning comprehargivee forensic review. </planning
eneasoninge The image shows structural inconsistencies such mismatched color tones, distorted shapes, proportions.
and alignment are logically placed, suggesting a natural scene, Upon closer inspection, the edges and textures appear smooth and coherent, with no signs artificial blending or unnatural blurring. Details like individual cars and text on the building are preserved, and the overall sharpness is uniform. There are no missing high. frequency textures or blocky artifacts typically intraduced by generative models. Deeper andlysis reveals that the lighting and shudows follow natural patterms, and the surnounding consistent. There stylization focus discrepancy that would Suggest generation artifocts. </readoning? <conclusione Considering absence visuel, subtle, or physical-level anomalies, the image maintoins high level of realism and coherence characteristic of an authentic cerial photograph. There no compelling evidence to saggest it fake, leading to the conclasion that it is most likely real image. </condlusion condwer> real </onswer>
proportiond
are realistic,
shodows
the read layout and vegetation-iie contextually
pest-processing
(Foke Image. from LOKI, satellite)
<fasts first glance, image presents unusually distorted aerial view, raising initial suspicion authenticity due apparent unnoturdiness.
<ploninng Given the initial impression of distortion, detailed onalysis is necessory to ascertain image's 's authenticity. This involves exomining structural coherence, assessing for subtic artifacts of artificial genenation, and evaluating structurd integrity. especially in areas where the distortion tion reveals inregularities that defy real- world geometry. comprehensive inspection is essentiol to uncover hidden aromalies indicutive of ddeepoke creation. </planning
structures, the building near the center-left of the image appears severely warped misplaced. angles proportions inconsistent with typical architectural the left side seemingly blending into the street boundary below, suggesting a mishondled synthesis or massaging error during deepfole creation, This kind of misalignment is clessic giveaway in identifying artificially altered imagery. Exomining the greenery across the scene, there is lock of coherent integration, where the shopes and textuuees do not harmorize with realistic environmental expectations, Trees appear muddled and blurred, os if the algorithm ihim struggled form distinct foliage from pixel data. These distartions might be aftributed or issues segmentation tosks typical AT-generated ed images. While astessing problem of perspective becomes apporent. The angle depth transitions between the foreground and background hold no adherence to a singular, logical vicwpoint. This likely due to Al's 's capacity to struggle with seamless integration across varying viewpoints, resulting in on image where depth appears more subje subjective than spatially accurate. This inconsistency in perspective further coments the likelihood of digital tompering or synthetic origins. Additionally, the traffic near the bottom of the image lacks realistic flow. reinforcing the idea that eloments realistically integrated </reasonings
creadoninge Starting
evaluction
inodequate training
image's overall geometnic consistency.
compressed
carefully aligned
aforementioned worping distortions structure and perspective contribute to a visual 'cognitive dissonance, where the human mind begins to question the image's 's hoality. The inaility to necognize typical structural features, coupled with unnaturai alending lines between elements, contributes to this effect. It's though the AI attempted to partroy organized space but failed execute complex realistic spatiel comprehension, leading to extrusion of visual cues detoching while subtie in implication, collectively betroy synthetic origits.
cneflections Upon deeper reflection, becomes evident
natural hormony. These
shawcesing challenges faces emuleting complex real-world enmentttnt with fidelity. </reflection <conclusione Despite initial suggestion of derial perspective, the image's 's dis distortions-- portinss--ootrrrrrrrya structure integrity, ceherence of natural elements, and perspective tive clignment- -oo----srreee Strongly signal its artificial
nature. These errors, especially when viewed col collectiog lively. unveil the image es on AI- -I-generated forgery. The chaotic blend of unnatural architecture and blureed green textures, coupled with problematic perspactive tronsitions, confirms the image as Such anomalies generally grise due its segmentation and construction
to-eeeet despfoke, illus trating common foilings in AT'S copobility
nender realistic, coherent challenges, offirming identificet cs without needing advanced scrutiny. </conclusion <onswer fake s/onswer>
Figure 26: Reasoning output of VERITAS on AIGC images.
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Published as a conference paper at ICLR 2026
(Real Image from LOKI, scene)
<fas At first glance, the image appears authentic with natural mountain and forest scenery under clear sky. </fast>
splaneings To assess authenticity, we proceed thrse stages: first evalucting obvious structural fectures, then checking for subtle AI ortifacts, and finally onalyzing for violations of physical realism. Since clear anomalies visible, detailed inspection necessary enzure hidden cluss suggest artificial generation, </planiing <reasoningo The mountain's mow cover surrounding terrain show signs distortion, thene ne mismetch between
inconsistency,
or unnatural blending The lighting and shadows olign with single light source, the sky, clouds, and landscopee Upon eloser inspection. the edges and textures are smooth and realisticc, with no artificial boundories or blur. The image retoins high-freguency detaill like sho texture and forest variations, which ane often missing in Al Ae-generated images. There are no signs of stylization or focus discrepancies that manipuctionn </readoning
would imply generation post observations, the image exhibits signs AI generction manipulation <conclusioro Synthesiiing
maintains visual, textural, and physical consistency with a real-world photograph. The initial judgment finel clossificat es real olign with the detoiled forensic evidence present. </cenclusior canswer> real </answer>
immediate determination of (Fake Image from LOKI, scene)
<fasts first glance, the image presents realistic paptivating scene, authenticity is not possible without desper inspection </fast>
splarnings Eiven the initial impression of realism, a systematic deep dive into the image is necessary. We start by excmining visual structural coherence and lighting. then proceed to detect subtle artifacts often introduced by AI, and finally assess whether any elements violate physical contextual plausibility. This layered oppreach ensures detail overlooked, especially since the image does reveal its nature superficially. s/planning
creadonings first, the landscape elements, without abvious mismatches color structure. shadows, and the integration between the bost and the water suggests dynamic inferaction. However, diving deaper into the finer details neveals poteential anomalies- -uuch as the smooth and uniform green coloration of the vegetation ond the shorpness of the riverbank edges, which lock some expected fed natural variction and texture seen real environments. The water surface seems extremely polished and reflective while the reflection itself everly perfect- perfett--sugeefiing synthetic material appearance that could be marker Al rendering. Additionally,
flow and surrounding greenery, appear natural light source on honizon creates realistically long
exhibit dramatic dynamic range in both hue texture that may be exeggerated for naturally occuriin scene, patentially indicating digital enhoncements typical of deapfake generation
techriques, A notable indication is the absence of natural imperfections such as weather effects on the water or veriations in foliage texture, further pointing towards the possibility of the image being AI Al-generoted Another layer of scrutiny reveals on unusual Interaction of light around the clouds and the herizon, where the shodows and highlights don't t trandition smoothly. This signify artificial attempt replicating lighting effects that world physics might produce consistently </reasoning creflectione 'ss
ervirormental context real
settings usually include ruanced
like floating particles, subtle abervations in reflections or minor shodows mismatched due to atmosphenic conditions. Here, the scene feels slightly stylized to on almast painted aesthetic, which, while masterfully done, diverges from the chootic unpredictability of actusl rature. This kind of stylization can be a telltale sign of AI involvement, disguising imperfections under venser of hyper-realian, Moreover, the image exhibits an amusing ekartyy cer tain aspects---Ba seettt--ikkee like the boot and river- are overly precise in detail compored other vogue elements the
creating incoharent focus balance that hyper-real yet artificial. align
optics.
Something feels subtly just right" that
<conclusior Even though image initially rodiates authenticity through its visuel coherence, the everly smooth texture of the water, consistent reflections, and stylistic blending of natural elements suggest an Al .e-generteed onigin The subtie discregancies in envirrmental inferactions and optical focus, although ruanced, point to tochnologies rother than on outhentic snapshot. Thus, despite its convincing realian, the meticulous exomination lon highlights its deepfoke nature, confirming as sophisticated AI creation, </conclusion condwer> fake /nnswer>
Figure 27: Reasoning output of VERITAS AIGC images.
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Pattern-awer -wwre SFT Data Annotation (Stage-1 Prompt for Fake Images)
(real image) [ake image) You are given two images:
1. A real face image captured by real-world camera.
2. A fake face image generated by AI, using (Forgery Type and Explanation
Please compare them visually and select two most noticeable abnormalities that best indicate the second image fake.
Choose from the following list: 1. Color Difference Inconsistent skin tone between facial regions (e.g., skin tone mismatches face-swapped regions).
Structure Abnormal
The structures of facial features (e.g.. eyes, nose, eyebrows, mouth) are distorted or asymmetric, which do not conform to the biometric patterns found in real human faces.
3. Edge Abnormal
Artificially sharp or jagged facial contours/edges, which are inconsistent with natural soft transitions.
4. Expression Abnormal
Unnatural or distorted facial expression which are common in real human faces (e.g., mismatched smile-eye movements).
Facial Proportion Abnormal
Unusual proportion of facial components (e.g., abnormally wide eye distance overly large forehead).
6. Texture Abnormal
Overly smooth/rough or discontinuous skin textures.
7. Blending Boundary
In blending techniques, the facial skin exhibits hard transitions or obvious block boundary.
8. Unnatural Blur
Abnormal local/block blurriness on facial regions or facial contour.
Frequerry Detail Missing
Although the image is in high-resolution and looks realistic, some human biological details (e.g., pores and fine hair) are missing.
10. Non-physically Plausible Lighting and shadow Inconsistent light/shadow directions across facial regions or light/shadow that violate physical illumination laws.
11. Contextual Element Mismatch
Incongruous background mismatched style/lighing between face and background.
12. Makeup Implausibility Conflicting specular highlights and makeup distribution (e.g., highlight non-glossy skin areas).
13. Holistic Stylization
In advanced entire face generation, the image looks realistic, but stylized overall (e.g.. hyper-realistic digital art).
14. Abnormal Optical Focus Discrepancy
Entirely generated faces sometimes fail simulate natural camera optics, exhibiting simultaneous sharpness in geometrically incompatible depth planes abrupt defocus transitions violating optical distance gradients.
Return only your selected clues, exactly as listed, separated by "," Please strictly follow the format.
Figure 28: Prompt for fake images SFT data annotation (Stage anomalies identification).
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Pattern-awe aware SFT Data Annotation (Stage-2 Prompt for Fake Images)
(image)
### Background Information
fake face image generated by AT has multiple possible artifacts. Generally, these can be divided into three categories.
1. Visual Structural Abnormalities: Obvious abnormalities that can be clearly perceived visually, typically including the following types:
1.1 Color Difference
Inconsistent skin tone between facial regions (e.g., skin tone mismatches in face- swapped regions).
2. Subtle Artifacts from Generator: Subtle artifacts introduced by generators that require more careful observation, typically including the following types:
2.1 Edge Abnormal
Artificially sharp or jagged facial contours/edges, which are inconsistent with natural soft transitions.
Violation of Physical Laws: Implicit artifacts that require deeper observation and thinking, connecting visual clues with common knowledge. This typically includes the following types:
3.1 Non- Non-physically Plausible Lighting and shadow
Inconsistent light/shadow directions across facial regions or light/shadow that violate physical illumination laws.
### Preliminary Observation
The given image is a fake image generated by (Forgery Type and Explanation). After careful inspection, we conclude two most noticeable abnormalities that indicate the image is fake:
(Stage-1 Resultss
#*# Task Definition
Your task examine the given image, then:
1. Give your initial judgement of the authenticity of the image:
Do not use any priori information provided above
If you think it is hard to make a judgment, you can point this out faithfully
Extract meticulous visual facts that mainly conform to above abnormalities. Specif- ically:
Take a careful examination of the given image.
Perform step-by-step forensics analysis according to the above three progressive categories.
3. Draw comprehensive conclusion based on your findings.
Keep your answer detailed and factual.
Figure 29: Prompt for fake images SFT data annotation (Stage visual facts forensics). The omitted parts in "Background Information" are consistent with the artifacts list in Figure 28
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Pattern-awar SFT Data Annotation (Stage-3 Prompt for Fake Images)
Your task is to convert the given information into logical chain-of-thought (CoT). The length and complexity should be conditioned on the given information.
### Extracted Evidence
The following information is the explanation the artifacts of fake image generated by AI. Specifically, we partition the explanation into three parts:
Initial Judgement
We have required previous model truthfully give the judgment first glance. If the image has obvious artifacts, it will generate certain judgment. Otherwise, will need further inspection.
The initial judgement current sample is as follows: (Initial Judgement from Stage-2)
Detailed Evidence
We cluster the possible artifacts into three progressive groups and then require previous model to conduct point-by-point forensic analysis. The extracted evidence of current sample is follows:
(Forensics Analysis from Stage-2)
3. Conelusion
Generally. for some ambiguous samples, we need to make a comprehensive judgement based on different aspects. Therefore, we require previous model draw comprehensive conclusion based on the extracted evidence.
The conclusion current sample as follows:
(Conclus us on from Stage-2)
### Task Definition
Your task to convert the given information into logical Chain-of-Thoure (CoT).
You not given the image, and you should keep faithful to the above information. You can flexibly decide whether to perform Long-CoT or Short-CoT, based on the sample's diffieulty.
Long-Coi Co'T: Hard samples need to generate comprehensive and logical reasoning content, Often follow structured pattern: Fast Judgement (<fast>)- Problem Planning <plaaninn>>) Evidence Collection (<reasoning>) Conclusion Acconclusions
Short-CoT: Medium and easy samples need to generate brief yet critical reasoning content. Often follow a structured pattern: Fast Judgement (<fast>) Evidence Collection (<reasoning>) Conclusion (<conclusion>)
The following guidance is only useful when you need
For "Problem Planning", you should analyze the current state and draw a progres- sive and reasonable plan
For "Evidence Collection", you should convert the given evidence into logical and coherent content. Do mechanically perform step-by-step analysis using junctions like "first" and "next". Instead, make your reasoning smooth and natural
If you suppose the current sample is extremely hard, you can insert "Self- Reflection" pattern before "Conclusion": You can smartly move some hard-to- detect artifacts from "Evidence Collection" into this part. Use natural conjunctions like "However". "But wait", etc. Enclose in <ref lect ion> tags. The reflective content is not a "restatement" but discovering something new that you have not considered before, which should be coherent with your previous reasoning content
For "Conclusion". you should draw comprehensive conclusion finally
Figure 30: Prompt for fake images SFT data annotation (Stage 3: thinking patterns injection).
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Battern-award SFT Data Annotation (Stage-1 Prompt for Real Images)
(image)
### Background Information
The authentic images can be manipulated by Al for improper use. fake face image generated by Al may suffer from some of the following artifacts. Generally, these can be divided into three categories.
1. Visual Structural Abnormalities: Obvious abnormalities that can be clearly perceived visually, typically including the following types:
1.1 Color Difference
Inconsistent skin tone between facial regions (e.g.. skin tone mismatches in face- swapped regions).
2. Subtle Artifacts from Generator: Subtle artifacts introduced by generators that require more careful observation, typically including the following types:
2.1 Edge Abnormal
Artificially sharp or jagged facial contours/edges, which are inconsistent with natural soft transitions.
3. Violation of Physical Laws: Implicit artifacts that require deeper observation and thinking, connecting visual clues with common knowledge. This typically includes the following types:
3.1 Non-physically Plausible Lighting and shadow
Inconsistent light/shadow directions across facial regions light/shadow that violate physical illumination laws.
### Preliminary Observation
The given image is an authentic image captured by real-world camera. Besides, we divide the authentic samples into three difficulty levels: easy, medium and difficult. In general, easy samples have high visual clarity and are extremely realistic.
Samples of medium difficulty are in lower quality, but there are still strong evidence indicating its authenticity.
High- digh-difficulty samples are in low quality and may contain some misleading artifacts, requiring careful thinking and comprehensive judgment. The currently given image is considered as (di ffcuuty..
#H# Task Definition
Your task examine the given image, then:
1. Give your initial judgement of the authenticity of the image:
Do not use any priori information provided above
If you think it is hard make a judgement, you can point this out faithfully
Provide an explanation that can distinguish the given real image from fakeness. Specifi- cally:
Take a careful examination of the given image
Perform step-by-step reasoning according to the above three progressive categories
If there exits some misleading artifacts in the given image, you can point them out truthfully
3. Draw comprehensive conclusion based on your reasoning
Keep your answer detailed and factual.
Figure 31: Prompt for real images SFT data anmotation (Stage 1: visual facts forensics). We divide the difficulty of real images upon datasets. FFHQ and CelebAHQ considered simple for their clear visual details. FaceFoornsicc** and CelebA are classified as medium for their miss visual details. LFW is considered hard for its low resolutions and unexpected noises. The omitted parts in "Background Information" are consistent with the artifacts list in Figure 28
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Pattern-aware SFT Data Annotation (Stage-2 Prompt for Real Images)
(image)
Your task to convert the given information into logical chain-of-thought (CoT), The length and complexity of the reasoning chain should be conditioned on the given informa- tion.
### Extracted Evidence
The following information is detailed explanation that distinguishes given real image from fakeness.
Specifically, we partition the explanation into three parts:
1. Initial Judgement
We have required previous model truthfully give the judgement at first glance. If the image has obvious artifacts, will generate certain judgement. Otherwise, will need further inspection. The initial judgement of current sample is follows: (Initial Judgement from Stage-1)
Detailed Evidence
We cluster the possible artifacts into three *progressive*** groups and then require previous model conduct point-by-pon forensic analysis. The extracted evidence current sample is as follows:
(Forensics Analysis from Stage-1)
3. Conclusion Generally, for some ambiguous samples, need to make comprehensive judgement based on different aspects. Therefore, we require previous model to draw comprehensive conclusion based on the extracted evidence. The conclusion of current sample as follows: [Conclusion from Stage-1)
### Difficulty Information
To enable better control of the length of the reasoning chain, divide the authentic samples into three difficulty levels; easy, medium and hard.
In general, easy samples have high visual clarity and are extremely realistic. Medium samples are in lower quality, but there are still strong evidence indicating its authenticity. Hard samples are extremely low quality and may contain some misleading artifacts, requiring careful thinking and comprehensive judgment. The currently given image is roughly classified as (di culty).
### Task Definition
You not given the image, and you should keep faithful to the above information. You can flexibly decide whether to perform Long-CoT or Short-CoT, based on the sample's difficulty.
Long-CoT: Hard samples need to generate comprehensive and logical reasoning content. Often follow structured pattern: Fast Judgement (<fast>)- Problem Planning (<planning>). Evidence Collection (<reasoning>) Conclusion (<conclusion>)
Short-CoT: Medium and easy samples need to generate brief yet critical reasoning content. Often follow a structured pattern: Fast Judgement (<fast>)- Evidence Collection (<reasoning>) Conclusion (cconclusion)
The following guidance is only useful when you need it:
For "Problem Planning", you should analyze the current state and draw a progres- sive and reasonable plan
For "Evidence Collection". you should convert given evidence into logical and coherent content. Do not mechanically perform step-by-step analysis using junctions like "first" and "next". Instead, make your reasoning smooth and natural
If there are any misleading artifacts the provided information, should put them into "Reflection" pattern before "Conclusion", using natural conjunctions like "However". "Although", etc. Enclose in <reflect ion> tags. ONLY insert "Re- flection" pattern when there are known misleading artifacts.
For "Conclusion", you should draw comprehensive conclusion finally
Figure 32: Prompt for real images SFT data annotation (Stage 2: thinking patterns injection).
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Reasoning quality (Score evaluation)
You are a helpful assistant proficient analyzing vision reasoning problems.
## Instruction: Please examine the provided image attentively and serve as an unbi- ased judge in assessing the quality of the response from Al assistants regarding the instruction. You will receive single response from the assistant to user's instruction.
## Noticement: Your assessment should identify whether the assistant effectively ad- heres the user's instructions and addresses the user's inquiry. In your evaluation, weigh factors such as preciseness, comprehensiveness, clarity, creativity, and the granularity of the responses.
Do not allow the length of the responses to influence your evaluation.
not favor certain names or positions the assistants. Be as objective as possible.
## Criteria:
Use scores to show the quality of the response. Here is the detailed scoring rubric for evaluating the quality of responses from Al assistants:
Incorrect (1): The answer is incorrect (e.g., predicting real for the fake image), the score should be 1.
Poor (2); The response fails address the query effectively, Although the answer is correct, the reasoning process is vague and repetitive. Creativity and granularity are absent or poorly executed.
Average (3): The response shows superficial analysis of the given image. It lacks depth in creativity and granularity. The reasoning process is not detailed.
Good (4): The response is well-aligned with the given image, demonstrating high degree of preciseness and comprchensiveness. shows creativity and a nuanced understanding of the topic, with detailed granularity that enhances the response quality.
Excellent (5): The response shows great preciseness, comprehensiveness, creativity, and granularity. provides an insightful, detailed, and thorough answer, indicating deep and nuanced understanding of the user's inquiry.
## Desired Output Format:
Present your verdict in JSON format, with the key 'analysis' for a short reason of your judgement and the key 'judgment' to indicate your decision: use "[[1]]", "[[2]]", "[[3]]", "[[4]]", "[[5]]" indicate your evaluate score.
## Question:
(image) ) This is a (Ground Truth) image. The following are the user's query and the model's 's output.
[The Start of User Instruction]
Please determine the authenticity of this image.
[The End of User Instruction]
[The Start of Assistant's 's Answer]
[Model's s Reason.ng Output
[The End of Assistant's Answer]
Figure 33: Prompt for reasoning quality evaluation (Score evaluation).
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Reasoning quality (Pairwise evaluation)
You are a helpful assistant proficient analyzing vision reasoning problems.
## Instruction:
Please examine the provided image attentively and serve unbiased judge in assessing the quality of responses from two Al assistants regarding the user's question shown beneath the image.
## Noticement:
Your assessment should identify the assistant that more effectively adheres to the user's instruction and provides more detailed, more precise and high-quality reasoning. In your evaluation, weigh factors such as preciseness, comprehensiveness, clarity, creativity, and the granularity of the responses.
Avoid any position biases and ensure that the order in which the responses were presented does not influence your decision.
Do not allow the length of the responses influence your evaluation. Do not favor certain names the assistants. Be as objective as possible.
## Desired Output Format:
Present your verdict in JSON format, with the key 'analysis' for short reason of your judgement and the key 'judgment" to indicate your decision: use "[[A]]" if assistant A prevails, "[[B]]" if assistant B does, and "[[C]]" for tie.
## Question:
(image) ) This is a (Ground Truth) image. The following are the user's query and the model's output.
[The Start of User Instruction]
Please determine the authenticity of this image.
[The End of User Instruction]
(The Start of Assistant A's Answer]
(Model A Reasoning Output)
[The End of Assistant A's Answer]
[The Start of Assistant B's Answer]
(Model B' s Reasoning Output]
[The End of Assistant B's Answer]
Figure 34: Prompt for reasoning quality evaluation (Pairwise evaluation).
Generation of Personalization Prompts
(image) }
Your task create customized prompts for the input image to fool deepfake detectors.
### Requirements
1. Tailor the prompt based on the specific input image. Change the context. For example, "Old man with beard", "a chef bustling kitchen, exuding expertise and dedication", "beautiful bride, traditional, attire, floral braid, sequin headdress, orchid backdrop, pastels", etc.
2. Keep the prompt concise and effective.
3. Avoid using any obscure words.
Figure 35: Input for generating customized personalization prompts.
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Prompt for Reflection Quality Reward Model
(image) }
You are provided with an image and question for this image. The provided response the reasoning process of determining its authenticity.
You should re-examine the image carefully, and then review the self-reflection content enclosed in the <reflections >on> </ref. s/reflecte rrefecciionn ion> tags:
1. Is the reflection content redundant with the reasoning content? Is the reflection content just restatement or conclusion of previous reasoning? The reflection should introduce new insights rather than restatement.
The reflection should not vague statements such as "too perfect" or "lack of imperfec- tions". Instead, it should be specific and detailed.
From 0 to 100, how do you rate for the reflection quality? Be strict, give low score it is not aligned with the above principles. Provide few lines for explanation and the rate number at last after "Final Score:".
Your task provided as follows:
Question: [ [Question]]
Response: [[Reasoning Output)] }
Figure 36: Prompt for Reflection Quality Reward model (UnffieRReward-Qwen-3B))))
Input Prompt for Veritas (All stages)
### System: You are an image authenticity expert. Your task to determine the authenticity the given facial image.
Firstly, give an overall judgement to the authenticity of the image, enclosed in <fast> </fast> tags.
Then, make a careful and structured thinking before reaching answer. Based your thinking, draw comprehensive conclusion. Enclose the corresponding part in different tags, e.g., <planning> or <reasoning> <reflectionns inne> or <conclusion>. Finally, give the final answer with "real" or "fake", enclosed in <answer> </answer> tags.
### User: (image) Please determine the authenticity of this image.
Figure 37: Input prompt for Veritas. The prompts for all training stages are consistent.
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Input Prompt for Qwen2.5-VL-7B Intern and GLM-4.VV-BB-Thnnke
### System:
You are given facial image. Please analyze the provided facial image and determine
whether it is authentic or fake based on the following classification criteria:
Real Captured Facial image
Images captured using a real camera or device without any alternations or manipulation.
Fake Facial Image
Images generated or manipulated using digital technologies, such as deepfakes, face swapping. face reenactment, photo editing software, entire face synthesis, etc. Output the thinking process in <<think>) (</think>) } and final answer ("real" or "fake") in <<answer>) {</answer>) tags, i.c., the output answer format should be as follows:
(<thinking>) your thinking process here (</thinking>) (<answer>) your judgement here [</answer>) Please strictly follow the format.
### User:
(image) } Please determine the authenticity of this image.
Figure 38: Input prompt for Owen2.5-VL-7B, Intern VL3-8B and GLM4
Input Prompt for MiMo- VL-7B
#### System:
You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
### User:
(image) ) Please determine the authenticity of this image. Output your final answer ("real" or "fake") in <answer> </answer> tags
Figure 39: Input prompt for MiMo-VL-7BB We found that providing priori knowledge does good for MiMo VL-7B, hence we remove any priori in system prompt.
Input Prompt for GPT-4o and (eemini-2.5-PP
### System:
You are an image authenticity expert. Your task is to determine the authenticity of the given facial image.
Firstly, give an overall judgement to the authenticity of the image, enclosed in <fast> </fast> tags.
Then, make a careful and structured thinking before reaching an answer. Based on your thinking, draw comprehensive conclusion. Enclose the corresponding part in different tags, e.g. <pl anni ng> or <reasonic ig?>> ng> or <reflect. on? or <conclus oconclusion>. on>.
Finally, give the final answer with "real" or "fake". enclosed in <answer> </ </answer> tags.
#C#
User: (image) ) Please determine the authenticity of this image.
Figure 40: Input prompt for GPT-4o and Gemini-2.5-Pro. Similar to MiMo-VL-7B. we found that providing priori knowledge is not helpful. We keep the default system prompt and only customize user prompt by constraining the output format.
58


Reviews:
[{"review_id": "hWBtcmkj04", "reviewer": "Reviewer_bRzS", "summary": "This paper first introduces the HydraFake dataset, which aggregates real and fake images from existing sources and by reimplementing and crawling 10K deepfake samples produced by 10 advanced generators, resulting in 50K real and 50K fake images. It also proposes a two-stage training pipeline for MLLM-based deepfake detection. In the first (SFT) stage, MiPO is introduced to internalize reasoning patterns; in the second stage, the pattern-aware GPRO promotes comprehensive reasoning and enables potential self-reflection. Extensive experiments on in-domain, cross-model, cross-forgery, and cross-domain evaluation sets demonstrate the effectiveness of the proposed method.", "questions": "see weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "a.The proposed dataset spans diverse domains and sources of real and manipulated images, including generative face‑swapping, visual autoregressive models, and deepfakes collected from social media.\nb.The two‑stage training pipeline substantially outperforms existing deepfake detection methods, as demonstrated in Table 1.", "weaknesses": "a.The proposed method employs SFT and GPRO within an MLLM‑based deepfake detection framework—an established post‑training strategy.\nb.The difference between the proposed pattern \"<fast><planning><reasoning><conclusion>\" and the commonly used \"<think>... </think>\" paradigm has not been analyzed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZdQeP2pw55", "reviewer": "Reviewer_MgHh", "summary": "This paper introduces HydraFake, a deepfake detection dataset with hierarchical OOD evaluation (in-domain, cross-model, cross-forgery, cross-domain), and VERITAS, an MLLM-based detector using pattern-aware reasoning. VERITAS employs five thinking patterns (fast judgment, planning, reasoning, self-reflection, conclusion) trained via a two-stage pipeline: pattern-guided cold-start with MiPO and pattern-aware exploration with P-GRPO. The approach shows significant improvements in cross-forgery and cross-domain scenarios, which are important in real-life scenarios.", "questions": "None", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The proposed HydraFake dataset addresses a crucial gap between academic benchmarks and industrial deployment scenarios, making it valuable for real-world applications.\n- The authors have conducted comprehensive experiments, including comparisons with SOTA methods and detailed ablation studies, verifying the effectiveness of the proposed VERITAS model.\n- The pattern-aware reasoning approach is reasonable, drawing inspiration from human cognitive processes to create more interpretable and robust detection systems.", "weaknesses": "- The two-stage training pipeline with MiPO and P-GRPO, though modified for forgery detection tasks, still seems to be a direct application of vanilla DPO and GRPO methods, which may undercut its novelty.\n- The paper evaluates VERITAS exclusively on the proposed HydraFake dataset, raising concerns about overfitting to their specific evaluation protocol. It would strengthen claims about VERITAS's superiority and provide more convincing evidence of its effectiveness if authors could perform evaluations on other benchmarks such as LOKI [cite 1] and Forensics-bench [cite 2].\n\n[cite 1] LOKI: A COMPREHENSIVE SYNTHETIC DATA DETECTION BENCHMARK USING LARGE MULTIMODAL MODELS. In ICLR 2025.\n\n[cite 2] Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models. In CVPR 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nXNZhwATM0", "reviewer": "Reviewer_dw7z", "summary": "This paper makes two primary contributions to the field of deepfake detection. First, it introduces HydraFake dataset, which extends the evaluation into a rigorous and hierarchical setting, providing a more realistic and challenging benchmark to the field. Second, the paper proposes Veritas, a novel MLLM-based detector. Experiments show both qualitatively and quantitatively the effectiveness of the model.", "questions": "1.\tIn Figure 1, the proposed Veritas can perceive the textual anomalies. Is such type of analysis contained in the training set? If not, can the base model conduct similar analysis?\n\n2.\tIn the MiPO stage, how is the image selected? What if removing the non-preference $s_l^{\\phi}$ in the training data? It would be great to provide some case studies.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- The proposed dataset is well-motivated. The division of four evaluation levels (i.e., in-domain, cross-model, cross-forgery and cross-domain) is reasonable. A fine-grained evaluation protocol is critical and reasonable at the moment, and the constructed dataset is of high quality, providing a challenging evaluation suite for the community.\n- The proposed pattern-aware reasoning is effective and insightful compared to previous explainable methods. Experiments clearly show its superiority compared to previous pipelines.\n- The proposed MiPO and P-GRPO is coherent with the proposed reasoning framework. Thorough ablations are conducted to validate their effectiveness.", "weaknesses": "- The authors should provide some failure cases to understand the model’s limitations.\n- More fine-grained ablations on the reasoning patterns could be done, e.g., what if removing the “reflection”/“planning” pattern?\n- How \"reflection\" improves model's generalization capability to unseen forgeries? The author should provide more explanations to it.\n- The human has very good reasoning capabilities. Why even human cannot accurately detect some (realistic) deepfakes? Is semantic-level reasoning capability truly crucial for deepfake detection?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UnnOwhnUMz", "reviewer": "Reviewer_wJ4c", "summary": "The paper introduces the HydraFake dataset and the VERITAS model.\nThe HydraFake dataset encompasses a diverse range of deepfake generation techniques and real-world forgery cases. It provides a rigorous training and evaluation protocol that covers unseen model architectures, emerging forgery techniques, and novel data domains.The VERITAS detector, built upon a Multimodal Large Language Model (MLLM), differs from conventional Chain-of-Thought (CoT) based reasoning frameworks. It employs a two-stage training pipeline — pattern-guided cold start followed by pattern-aware exploration — with a particular emphasis on key reasoning modes such as “planning” and “self-reflection”.By simulating the human forensic reasoning process, VERITAS integrates the cognitive capabilities of large language models into deepfake detection (DFD), thereby achieving enhanced cross-manipulation and cross-domain generalization performance.", "questions": "Q1: Could the authors provide additional comparisons or discussions with recent large multimodal reasoning-based detectors (e.g., FakeShield, M2F2-Det, SIDA) to better contextualize VERITAS?\n\nIf this question is properly addressed, it would increase my score, as a fair comparison with large models is the most critical issue.\n\nQ2: Does the proposed VERITAS framework have the potential to generalize beyond face forgery detection — for example, to AIGC-generated or other cross-domain manipulation tasks?\n\nIf this aspect is explored or discussed, it would further raise my score, though it is secondary to the first point.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed VERITAS framework demonstrates strong innovation in the field of Deepfake detection. Its core contribution lies in transforming the traditional feature-memorization-based detection paradigm into a reasoning-based framework grounded in pattern-aware reasoning.Unlike previous models that rely on specific forgery artifacts, VERITAS explicitly models forgery patterns as transferable reasoning units, enabling the understanding of common structural characteristics shared across different forgery types. In addition, the authors introduce a Chain-of-Thought (CoT) reasoning mechanism, allowing the model to progressively identify manipulated regions through multi-step logical inference, thereby enhancing interpretability and robustness.\nThe paper further proposes a Mixed Preference Optimization (MiPO) strategy, which balances the learning preferences among various forgery patterns, effectively improving the model’s cross-manipulation and cross-domain generalization capabilities.Overall, this study presents substantial innovation and academic value across three dimensions — detection paradigm, reasoning mechanism, and optimization strategy.", "weaknesses": "1. Lack of fair comparison with recent large-scale multimodal frameworks.\n\nAlthough VERITAS demonstrates impressive performance, the experimental comparison is incomplete and lacks fairness.\nThe paper does not include evaluations against recent large-scale multimodal or reasoning-based deepfake detection frameworks, such as FakeShield (ICLR 2025), M2F2-Det (CVPR 2025), and SIDA (CVPR 2025).\n\nThese methods share similar reasoning or multimodal fusion paradigms and therefore represent the most relevant baselines for comparison.\n\nIn contrast, most of the reported baselines are lightweight detectors (with only tens or hundreds of millions of parameters), while VERITAS is built upon an 8B-scale MLLM.\n\n\n2. Limited domain coverage.\n\nThe current work focuses exclusively on face-oriented deepfake detection, whereas a growing number of studies have begun exploring cross-domain and generalized forgery detection, such as AIGC-generated content, IMDL (image manipulation localization), and general multimedia forensics frameworks (e.g., Effort, FakeShield, ForensicHub(NeurIPS 2025) ).\n\nExpanding the scope of VERITAS to handle non-facial or multi-domain forgery types would not only enhance its practical value but also align it with the emerging trend toward universal content authenticity verification.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)