Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation
Authors: Siwei Wen
Junyan Ye
Peilin Feng
Hengrui Kang
Zichen Wen
Yize Chen
Jiang Wu
wenjun wu
Conghui He
Weijia Li
Venue: NeurIPS 2025 poster ()
Keywords: Synthetic Image Detection
Deepfake
Large Multimodal Model
Primary area: applications

Abstract: With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, synthetic images have become increasingly prevalent in everyday life, posing new challenges for authenticity assessment and detection. Despite the effectiveness of existing methods in evaluating image authenticity and locating forgeries, these approaches often lack human interpretability and do not fully address the growing complexity of synthetic data. To tackle these challenges, we introduce FakeVLM, a specialized large multimodal model designed for both general synthetic image and DeepFake detection tasks. FakeVLM not only excels in distinguishing real from fake images but also provides clear, natural language explanations for image artifacts, enhancing interpretability. Additionally, we present FakeClue, a comprehensive dataset containing over 100,000 images across seven categories, annotated with fine-grained artifact clues in natural language. FakeVLM demonstrates performance comparable to expert models while eliminating the need for additional classifiers, making it a robust solution for synthetic data detection. Extensive evaluations across multiple datasets confirm the superiority of FakeVLM in both authenticity classification and artifact explanation tasks, setting a new benchmark for synthetic image detection. The code, model weights, and dataset can be found here: https://github.com/opendatalab/FakeVLM.

Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation
Junyan Junnann Zichen Wen+.1 Yize Chen², Jiang Wu Wenjun Wu', Conghui He Weijia L
Siwei Wen
P Peilin Feng', Hengrui Kang*),
Shanghaii Artificial Intelligence Laboratory, Sun Yat-Sen University, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Shanghai Jiao Tong University, 'The Chinese University of Hong Kong, Shenzhen
Abstract
With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, synthetic images have become increasingly prevalent in everyday life, posing new challenges for authenticity assessment and detection. Despite the effectiveness of existing methods in evaluating image authenticity and locating forgeries, these approaches often lack human interpretability and do not fully address the growing complexity of synthetic data. To tackle these challenges, we introduce Fake specialized large multimodal model designed for both general synthetic image and DeepFake detection tasks. FakeVLM not only excels in distinguia guishingiingg real from fake images but also provides clear, natural language explanations for image artifacts, enhancing interpretability. Additionally, we present FakeClue, comprehensive dataset containing over 100,000 images across seven categories, annotated with fine- fine-grained artifact clues in natural language. LLM demonstrates performance comparable to expert models while eliminating the need for additional classifiers, making it robust solution for synthetic data detection. Extensive evaluations across multiple datasets confirm the superiority of VLM in both authenticity classification and artifact explanation tasks, setting new benchmark for synthetic image detection. The code, model weights, and dataset can be found here: https: : h//gtt/ub..../.t com//ppendatalll/FaaaeW...
Introduction
As Al-generated content technologies advance, synthetic images are increasingly integrated into our daily lives 2 Technologies like Diffusion 5 images. However, synthetic image data also poses significant risks, including potential misuse and social disruption [7). For instance, synthetic fake news 9 10 and forged facial scams [12 [3]
6 and Flux can generate highly realistic
make it increasingly challenging to discern the truth and establish trust in information. In response to these threats, the field of synthetic data detection has garnered widespread attention in recent years. However, traditional synthetic detection methods typically focus on simple authenticity judgments
18 19, ultimately providing only a forgery probability or classification result. This approach still has significant limitations in terms of human interpretability. As a result, users find it challenging understand the reasons behind the system's decisions, affecting the docision-making process's s transparency and trustworthiness.
14 15 16
The rapid development of large multimodal models has accelerated progress in synthetic data detection 20 21
23 24 25). These models, particularly, can provide explanations for authenticity judg- ments in natural language, thus laying the groundwork for enhancing the interpretability of synthetic
22
'Equal Contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NeurTPS 2025).

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detection. For instance, Jia et Ji al. 266 explored ChatGPT's ability to assess synthetic data, highlighting the potential of large models in this domain. LOKI [27 and Fakebench (28) further delved into the capabilities of large models in offering explanations for image detail artifacts. However, these studies primarily focus on pre-trained large models, utilizing strategies such as different prompt wordings to enhance model performance on this task rather than developing specialized multimodal model for this specific domain. Moreover, existing general large models still show a significant performance gap compared to expert models or human users in detection tasks.
Researchers have further explored and designed multimodal models specifically for synthetic data detection tasks [25] 24 27 29 30]. For instance, works like DD-VQA [23] and FFAA [24] focus
primarily on the performance of large models in Deepfake detection tasks, especially in the context of artifact explanation. However, their performance on more general types of synthetic images still
requires further investigation. Other works, such as Fakeshield (20) and ForgeryGPT [22], effectively examine the ability of large models to localize forgery artifacts and explain manipulated synthetic data. However, artifacts in forged synthetic images often concentrate in transitional areas, exhibiting more noticeable edge artifacts. In contrast, direct synthetic images are more likely to show structural, distortion, or physical artifacts, highlighting significant differences between the two. Additionally, current large models still lag behind expert models in pure authenticity classification. For example, studies such as X2-DFD [25] and FFAA 2 attempt to integrate traditional expert- synthetic detection methods to improve classification accuracy without fully unlocking the potential of large models in synthetic detection tasks.
To address the challenges outlined above, we introduce F.keVI.M, large multimodal model specif- ieally designed for fake image detection and artifact explanation. Fake focuses on artifacts generated from synthetic image models rather than forgery artifacts. Moreover, it is not limited to facing Deepfake tasks but extends to more general synthetic data detection. Notably, FakeVLM achieves performance comparable to expert models based on binary classification without requiring additional classifiers or expert models. Additionally, we introduce FakeClue, a dataset containing over 100,000 real and synthetic images, along with corresponding artifact cues for the synthetic images. FakeClue includes images from seven different categories (e.g., animal, human, object, scenery. satellite, document, face manipulation), and leverages category- knowledge to annotate image artifacts in natural language using multiple LMMs. Our main contributions are as follows:
We propose Fake VLM, a multimodal large model designed for both general synthetic and deepfake image detection tasks. It excels at distinguishing real from fake images while also providing excellent interpretability for artifact details in synthetic images.
We introduce the FakeClue dataset, which includes a rich variety of image categories and fine-griieed artifact annotations in natural language.
Our method has been extensively evaluated on multiple datasets, achieving outstanding performance in both synthetic detection and abnormal artifact explanation tasks.
2
Related Work
2.1
Synthetic Image Detection
Traditional synthetic detection tasks are typically approached as binary classification tasks [31 26
32
34 35), treating them as either true or false, based on data-driven methods, with various detectors such as CNNs (36) and transformers (37] Some research [38 39 40 has explored methods like learning anomalies in the frequency domain, reconstruction learning, and adversarial learning. The focus of traditional synthetic detection tasks has mainly been on addressing the generalization issue [15 16). where the training and testing domains differ, in order to counter the rapidly evolving synthetic models. However, these methods only provide authenticity predictions without offering detailed explanations behind the predictions.
33
Some works have gone beyond the fundamental true/false classification problem, focusing on inter- pretable synthetic detection. For example, gradient- based methods are used visualize highlighted regions of predictions [41 42 43). Alternatively, model designs like DFGNN [4] enhance inter- pretability by applying interpretable GNNs to deepfake detection tasks. Additionally, research has explored the detection and localization of forgeries by constructing image artifacts or modifying labels [17 18 19]. While these methods enhance model interpretability, using natural language to describe the identified reasons remains underexplored.

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Data Collection
Data Pre-processing
Label Prompt Design
Multiple LMMs annotation
Open-source
Fake
Real
are expert in synthetic detection and identification of image artifacts..
Data Prompt
Task
Owen2
[Caption 1]
Classification
M Tp:
Thit a take Image] Thhs real Image]
InternVL
[Caption 2]
Cerlmage
house synthesis
Chameller
Decarazt
Amira
1085
10M
1967
Tip: photo
the following Informations
deepsook
[Caption
Harre
Skin color transitions... Eyes eyebrows area.. Lpps and mouth...
Qwen2
Sore
Hed
[Caption]
Seeilvv
1400
knowwolge
Fase M
24
Overall farial contours...
Integration Rewrite
Sabellbee
Decument
Figure 1: Construction pipeline of FakeClue dataset, including data collection from open source and self-synthesized datasets, pre-processing with categorization, label prompt design based on category knowledge(Face M: Face manipulation), and multiple LMMs annotation with result aggregation.
2.2 Synthetic Image Detection via LMMs
Recently, the development of large multimodal models (LMMs) has been rapid 45 46 47
48
49
50
Models such as the closed-source GPT-4o [5T] and open open-source models like Intern VL 52 and Qwen2-
VI. [53] have demonstrated outstanding performance across various tasks, showcasing impressive capabilities. In the domain of synthetic data detection, several works like (66.. Fakebench (28), and LOKI [27] have investigated the potential of LMMs, where these large models not only deliver accurate synthetic detection judgments but also offer natural language explanations for their true/false predictions, enhancing the interpretability of synthetic detection. However, these studies mainly focus on evaluating pretrained large models rather than training expert multimodal models. Furthermore, existing general models still lag behind expert models or humans in detection tasks.
An increasing number of studies have further explored the use of LMMs for synthetic detection, such as DD-VQA (33 and FFAA [24] have explored the performance of large models in the deepfake domain. However, their performance on general synthetic images remains less explored, Fakeshield 20. SIDA 21 and ForgeryGPP [22] investigate the ability of LMMs to detect/explain artifacts in manipulated synthetic data. However, tampering artifacts (mainly transitional), while direct image synthesis artifacts tend to involve structural distortions other types of image warping, which differ significantly. Furthermore, current LMMs underperform expert models in simple real/false classification tasks (25 26]. For example, studies like X2-DFD (25) and FFAA [4 have combined traditional expert synthetic detection methods with large models to improve classification accuracy.
3 Dataset
3.1 Overview
We introduce FakeClue, multimodal synthetic data detection benchmark for general and DeepFake detection. It includes two tasks: synthetic detection and artifact explanation, requiring models to determine image authenticity and explain its artifacts. FakeClue covers 7 image categories with over 100k samples. Utilizing a multi-LMM labeling strategy with category priors, FakeClue provides image-cat cagtion pairs for the image and its natural language artifact explanation. It emphasizes direct synthesis over tampered image artifacts. Training/test sets are randomly split; the test set contains 5,000 diverse image samples. Detailed dataset information is provided in the supplementary materials.
3.2 Construction of FakeClue
Data Collection: As shown in the data collection phase of Figure I FakeClue has two data sources, including open synthetic datasets and our newly synthesized data for specialized types. For open synthetic datasets, we extracted approximately 80K data from GenImage [54], FF++ (55] and
Chameleon [56]. maintaining a 1:1 ratio of fake to real data. For specialized types of data, such as remote sensing and document images, we generated the data ourselves. We collected remote
methods 62 using GAN and Diffusion--aase 663 64), covering urban, suburban, and natural scenes. For document images, we adopted layout- first, content-rendering approach, generating synthetic images of newspapers, papers, and magazines with real-world data sourced from the M6Doc dataset 65].
sensing images from public datasets (57 58 59 60
3

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Data Pre-Processing: At this stage. we first categorize the collected raw image data based on authenticity labels. Since the data from Genlmage and Chameleon lack category information, we use classification model [66] to divide the data into four categories: animal, object, human, and scene. The FF++ dataset corresponds the face manipulation category, while the newly synthesized satellite and document data also have clear category divisions. The distribution and proportions of these categories are shown in Figure The labels obtained during the data preprocessing stage will serve the foundation for the subsequent Label Prompt Design.
To overcome the hallucinations and limited synthetic detection capabilities of large models, we inject external knowledge to aid in artifact detection. Pre-processed authenticity labels serve as prior prompt knowledge. For real images, we analyze the plausibility of the image as a photographic result, while for fake images, we focus on detecting artifacts throughout the image. And classification labels are used as category-specifie knowledge. This knowledge comprises predefined focal points and common artifact types pertinent to each category. guiding the model's s attention to key areas, such as the artifact detection cues for facial images, as design 14 distinct types of prompts to address various authenticity
Label Prompt Design based on category knowledge:
shown in Fig. [ Accordingly. and category labels. In addition, for synthetic images that may have high quality and no visible artifacts (e.g., images from the Chameleon dataset), use special prompts prevent the model from forcing interpretation (see Appendix H for detailed prompts for different categories).
Multiple LMMs Annotation: To mitigate the bias or hallucination effects of a single multimodal model, we adopt strategy of annotating and then aggregating results from multiple high seggh-erformance open-source large models. For given image I, from the set ( 1,1, we first use three large multimodal Qwweel-- Intern VL, and Deepseck-to generate a set of candidate artifact captions = [4), 1, Each caption A E C, potentially highlights different aspects of image artifacts.
To synthesize a unified annotation A, from candidate captions C, = [A,, AA, A J. we employ Qwen2- VL aggregation. denoted as Magg This model is prompted with specific instructions Pinstr (detailed in the Appendix H to identify consistent artifacts across C, and remove redundant information. Thus, the final aggregated annotation A, for image I, obtained as:
A; Mange (Ct, P (instr)
Where A is the k-th candidate caption for 1,, and Magg aggregates C, using prompt Pinstr (enforcing cosistencylnon-reduddaag into the final annotation A,
The model extracts common points from multiple model responses, filters out irrelevant observations that appear in only one model (unless they are critical, like glaring artifacts) and organizes them hierarchically by categories (e.g., texture, geometry, lighting), before outputting in a fixed format.
Table 1: Comparison with existing synthetic detection datasets (DF: DeepFake, Gen: General, Syn: Synthesis, Tam: Tampering).
Dataset
Field
Artifact
Annotator
Category
Number
DD-VQA
26
DF
Syn
Human
5k
FF-QQA
24
DF
Syn
GPT
95K
LOKI 27
Gen
Syn
Human
3k
Fakebench
28
Gen
Syn
Human
6k
MMTD-Set
20
Gen
Tam
GPT
100k
FakeClue
DF+Gen
Syn
Multi-LMMs
100k
3.3 Comparisons with Existing Datasets
Table presents a comparison of the synthetic detection performance of FakeClue and existing evaluated LMMs datasets. FakeClue offers broader domain coverage and, unlike DD-VQA, is not confined to Feep detection, featuring well-defined categories including specialized types like satellite images and documents. In contrast, LOKI and Fakebench are limited by the number of annotations and can only serve as evaluation sets. Compared to the recent MMTD-Set dataset, FakeClue focuses more on directly synthesized image artifacts rather than tampered artifacts.

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4 Method
In this section, we first analyze the challenges of large multimodal models in synthetic image detection (Section 4.1p. p. Then, provide a detailed description of the FL architecture (Section 4.2) and training strategy (Section
Describe areas of synthetic Arimal imagery
1 Animal structure and physical feature accuracy
Does the image look real fake?
1 Surtace texture and color consistency
(3) Object environmental realism
+ Symmetry and consistency
Image
? Overall color and image processing accuracy
Encoder
V Projector
[Ground Truth] This take image.
The zebra stripes inconsisten some areas appearing overly smooth others too rouch: @ sexture
zobra's skin incomnistnti 3 the overall color balance unnaturel, with overly saturated colors... @ background Blurry and lacks everall image qualityl is unnatural, with lack of realiticc details unrealitiic color gradient.
Strengthen Large Language Model
the
Take image. O zebra's stripes are inconsistent unreaistic, areas aspearing ing everly sharp the taxture balaence image some parts appearing aver-aaturated; background is overty blureed, which typical photograph of zebra natural habitatt the overall Image quality appears to unnatural highlights and shadows.
showing unnatural connect-ions belween stripes, the overall color
Our FakeVLM Response
Figure 2: Overview of FLM, our proposed framework for detecting synthetic images and explaining their artifacts. Built upon LLaVA, VLk integrates multiple captioning models to assess key visual aspects.
4.1 Re-thinking LMMs' Challenges in Synthetic Image Detection
Recent studies have shown that directly using LMMs for synthetic image detection still face significant challenges [27 28 26 Although large models possess strong text explanation capabilities, when
tasked with determining whether an image was Al-generated or identifying forged images from set, pretrained LMMs often fail to achieve satisfactory performance. This phenomenon highlights the difficulty of relying LMMs for authenticity judgment, which is closely related to the fact that these models are not inherently designed for synthctic data detection tasks. Nevertheless, through extensive pretraining tasks, multimodal large models have developed strong visual feature extraction abilities and alignment with text. This raises the question: do the internal representations these large models potentially encode information that can distinguish real images from synthetic ones?
Inspired by Zhang et al. 677, we explored a simple yet effective method on FakeClue: ex- tracting visual features from the last layer of pre- tra-trained LMM and training lightweight lin- car classifier to determine image authenticity, If the representations learned by the model indeed contain discriminative information related to au- perform
Aceuracy
Frozen LLaVA
37.2%
Frozen Linear
Real/ Fake QA
-35%
3774%%
thenticity, even simple classifier initial detection using these features. As shown in Figure 3 the results confirm that the LMM has potential in distinguishing authenticity. How- ever, in contrast to the conclusions of Zhang et al. [67]. framing the task as "Does the image look real/fake?" by using fixed answers like "Real" or "Fake" not only limits the model's ability to provide textual explanations but also results in suboptimal performance. This is likely due to large models' challenges in aligning complex visual content with such binary answers. Building on the dataset we constructed, we found that framing the task as visual question answering, where the model is required to provide not just "Real"/ answers but also explain the image "artifacts", leads to better alignment between the response text and the image content in Figure 3 (For detailed results, please refer to Appendix [B. This approach not only improves the performance of artifact explanation but also significantly enhances the overall performance of synthetic image detection.
Explanatory QA
40
30 60 70 60
Figure 3: Comparison of synthetic image detection approaches on LOKI and FakeClue datasets: (1) QA with Frozen LMMs (no training). (2) Frozen backbone + linear probe (only linear layer trained), (3) Direct Real/Fake QA tuning, and (4) VQA with artifact explanations tuning.
5

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4.2 Model Architecture
Our approach follows the architecture LLa as illustrated in the framework diagram (see Figure i the top-right corner), which consists of three core components: i) Global Image Encoder, ii) an MLP Projector, and iii) a Large Language Model (LLM). We detail each component as follows:
Global Image Encoder: We employ the pretrained vision backbone of CLIP-ViT(-144) [68 as our global image encoder. The encoder processes input images with resolution of 336x336 preserve synthetic artifact details, resulting in 576 patches per image.
CLIP-ViT(I) ER" Nxd,
where N pr denotes the number of patches (P = 14), and d, 1024 the feature dimension. Multi-modal Projector: A two-layer MLP adaptor bridges visual and textual modalities:
HW
GeLU W, + b,)
(3)
Z = 2 b2
where W E R 1024x4096 W2 E R 94496x4096 are learnable parameters. The projected features Z E Nx409 combine with text embeddings of the task prompt through concatenation.
Large Language Model: We utilize Vicuna-v1.5 a 7B language model, as our base LLM. Vicuna-v1.5 renowned for its strong instruction saauuction-following capabilities and robust performance across diverse tasks. To further enhance its reasoning abilities synthetic data, we perform full- parameter fine-tuning, optimizing the following objective:
T ((0) = - Σ 1=1
log Po (a, a IZ;
(4)
where E(-) denotes text embeddings. We update all parameters 0 of the LLM during training, enabling full adaptation to synthetic data reasoning while preserving original instruction-folooiing capabilities via full-parameter optimization
By integrating these components, our pipeline leverages the strengths of multimodal models and fine-tunes LLaVA to achieve robust performance in detecting and explaining synthetic data.
4.3 Training strategy
As described in the construction process of FakeClue, we leverage category knowledge and utilize multiple LMMs to annotate image artifacts in natural language. As shown on the left side of Fig. 2 we obtain high-quality artifact descriptions target outputs for the model. Then, we the QA pairs obtained after the multi-model annotation and summarization steps as our training data. Each data sample consists of: (1) an image I; (2) a standardized prompt P: "Does the image look real/fake?": (3) the aggregated answer A from the multi-model annotations.
Our model. initialized from LLa 7B weights [45, underwent full -araametet fine-tuning on our constructed QA dataset. The training is conducted for two epochs on eight NVIDIA A100 GPUs with a batch size of 32 per GPU using a 2e-5 learning rate with 3% linear warmup and cosine decay. This full fine-tuning adapted the model synthetic data detection/explanation nuances while preserving its general instruction- isstuction-follloinng capabilities.
Experiment
In this section, we introduce three additional datasets used in the experiments, alongside FakeClue, and describe our experimental setup. We then present FakeVLM's performance general synthetic and DeepFake detection tasks, as well as its ability to explain image artifacts. Finally, we conduct ablation studies and further exploratory experiments to assess the model's performance.

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5.1
Other Benchmarks
a recently proposed benchmark for evaluating multimodal large models general synthetic detection tasks. Beyond just distinguishing real from fake, LOKI also includes human manually annotated fine-grained image artifacts, enabling a thorough exploration of the model's ability to explain image artifacts. This inclusion allows us to verify, to extent, whether our model's perception of artifacts aligns with human cognition.
LOKI [2
FF++ [55] is a widely used benchmark dataset for facial forgery detection, containing face images and videos generated by different types of forgery techniques. The dataset includes forged data created using four common forgery methods: DeepFakes, Face2Face, FaceSwan wap, and NeuralTextures. We used the commonly employed C23 versions.
DD-VQA [23] is a new face-domain artifact explanation dataset leveraging human common-sens, perception for authenticity assessment. features artifacts like blurred hairlines and unnatural skin shadows. Built on FF++ data, DD-VQA employs manual artifact annotations in a VQA format, requiring models answer common -ensee questions about artifacts.
5.2 Experimental setup
Task Settings. LOKI serves as the evaluation set, while training conducted on the FakeClue dataset, with testing performed on LOKI. For FF++ and DD-VQA, we use their default training-test splits for evaluation. Evaluation metries cover two tasks: detection and artifact explanation. Classification accuracy is represented by Acc, Auc, and Fl scores, while artifact explanation accuracy is measured using CSS and ROUGE_L.
Compared Baselines. For tasks requiring both synthetic detection and artifact explanation (e.g.. FakeClue, DD-VQA, LOKI), we compared various general- purppse LMMs, including closed-source models like GPT-4 and open-source ones such as Qwen2-VL, LLaVA, InternVL2, and Deepseck-VLL, We also included the Common-DF method from the DD-VQA dataset. For pure synthetic data detection, we further compared with recent SOTA expert methods.
5.3 Universal synthetic detection
Table compares Fake VLM with leading penerol-purpose LMMs and expert models, demonstrating its superior performance in both synthetic detection and artifact explanation. Specifically, compared to the current powerful open-source model Qwen2-VL-72B and leading expert model NPR AIDE, which is also trained on FakeClue, FLk achieves an average improvement of 7.7% % in Acc and 3.6% in F1 both FakeClue and LOKI. Additionally, LOKI includes extra evaluation metric for human performance, with an Acc 80.1 %, whereas FakeVLM achieves an Acc 84.3%, surpassing
Table 2: The experimental results on the FakeClue and LOKI datasets include both Detection and Artifact Explanation performance. denotes methods trained on FakeClue.
FakeCluc (Ours)
LOKI Evaluations (ICLR 2025)
Method
Acc
F1 1
ROUGE _
CSS
Acc
F1
ROUGE
CSS
Deepseek-VL2-ssmee
69
40.4
54.2
17.1
50.4
25.3
38.7
16.4
39.1
Deepseek-VL2 669
47.5
54.1
17.2
50.5
43.1
39.2
16.9
38.8
InternV122-88
49.0
18.0
58.1
52.6
47.2
Intern VL.240BB
[52
50.7
46.3
17.6
55.2
50.7
37.6
18.4
47.3
Qwen2-VL--BB [53]
45.7
59.2
26.6
56.5
57.1
35.0
18.2
38.4
Qwen2-VL--2B [53]
57.8
56.5
17.5
54.4
55.4
40.9
17.3
43.2
GPT-4o (2024-08-06) 5
47.4
42.0
13.4
40.7
63.4
57.2
35.4
CNNSpot (70
43.1
9.8
43.1
11.4
FreqNet [7]
39.3
58.9
Fatformer 72
54.5
45.1
58.8
48.4
UnivPD 15
63.1
46.8
49,0
35.8
AIDE 56
85.9
94.5
65.6
80.2
NPR* 773
91.6
77.4
80.0
Fake VLM
98.6
98.1
58.0
87.7
84.3
83.7
20.1
58.2

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Figure Synthetic image detection cases, covering animals, people, objects, documents, and remote sensing (red denotes incorrect, green denotes correct detection). Fa outperforms GPT in
precision, comprehensiveness, and relevance, demonstrating superior detection and interpretation.
human performance. This is likely attributed to Fake ability to capture deep, image- features that are imperceptible to the human eye for accurate authenticity judgment. Moreover, Fake strong ROUGE L and CSS scores the human-annotates LOKI dataset highlight alignment with human artifact perception, a noteworthy achievement given its training on entirely L.M-ggenrrtted data, which is largely thanks to our meticulously designed data annotation pipeline.
Figure VLM'ss qualitative evaluation. FakeVLMM identifies synthesis issues (e.g., visual artifacts, texture distortions, structural anomalies) with detailed natural language explanations. Unlike probabilit--ttreshold methods, VLke' intuitive, interpretable descriptions, enhance detection transparency, enabling confident and reliable synthetic content assessment.
We also present the generalization experiment results of Fake VLM on DMimage following Huang et al. (2I] The experimental results show that the performance gap between VLM and other expert models is not significant, with VLM even outperforming some of them. VLM does not rely on additional classifiers or expert models, yet it achieves performance comparable to or even exceeding that of expert classifiers while retaining its language capability for artifact explanation. This demonstrates the potential of large models in synthetic detection.
74 in Table 3
Table 3: Comparison with other detection methods on the DMimage weights for each method.
74
dataset, using the original
Real
Fake
Overall
Method
Acc
F1
Acc
F1
Acc
F1
CNNSpot 70
87.8
88.4
28.4
44.2
40.6
43.3
Gram-Net 775
62.8
54.1
78.8
88.1
67.4
79.4
Fusing 8
87.7
86.1
15.5
27.2
40.4
36.5
LNP (76)
63.1
67.4
56.9
72.5
58.2
68.3
UnivFD 15
89.4
88.3
44.9
61.2
53.9
60.7
AntifakePrompt 77
91.3
92.5
89.3
91.2
90.6
91.2
SIDA 21
92.9
93.1
90.7
91.0
91.8
92.4
FL
98.2
99.1
89.7
94.6
94.0
94.3
8

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5.4 DeepFake defection
We evaluate Fake VLM's performance on DeepFake detection, with results on DD-VQA presented in Table 4 FakeV VLM surpasses both general-purpe multimodal models and the specialized vision- language model, Common-DF, with improvements of 5.7% Acc, 3% in F1, and 9.5 % in ROUGE_I.
Table 4: The experimental results were evaluated on the DD-VQA datasets. Common-DF-T and Common-Doe Daamon-DF-IIaa represent text or image contrastive losses, respectively, while Common-DF-TI denotes both text and image contrastive losses.
DD-VQA (ECCV 2024)
Method
Acc
F1 1
ROUGE L
CSS 1
InternVL2-23
[52]
56.9
53.1
14.3
51.1
Intern V1.2-40B
[52
52.5
57.7
22.2
54.5
Qwen2-VL--77 [53
45.7
58.9
26.6
56.5
Qwen2-VL--2BB
59.5
57.9
20.5
56.6
GPT-4o [51]
53.2
31.7
13.0
42.7
Common-DF-1 78
83.7
87.6
57.7
Common-DF-- 788
84.9
88.4
58.8
Common-DF-TI 78
87.5
90.1
60.9
V.
93.2
93.1
70.4
86.6
Table shows FakeVLM's evaluation on the FF++ DeepFake detection dataset, including sub- categories like DeepFakes, Face2Face, FaceSwap and NeuralTextures. The experimental results demonstrate that Fake VLM continues to exhibit strong performance in these tasks, comparable to or even surpassing leading deepfake expert models. It not only leads in overall detection but also exhibits balanced performance across categories, avoiding overfitting.
Table Performance evaluation of Fake VLMM on the FF++ DeepFake detection dataset. The results highlight Fake VL'''s robust detection performance, on par with or outperforming top expert models.
FF++ (ICCV 2019) AUC(%)
Method
FF-DF
FF-F2F
FF-FS
FF-NT
Average
FWA [79]
92.1
90.0
88.4
81.2
87.7
Face X-ray 80
97.9
98.7
98.7
92.9
95.9
SRM 81
97.3
97.0
97.4
93.0
95.8
CDFA 82
99.9
86.9
93.3
80.7
90.2
Fake VLM
97.2
96.0
96.8
95.0
96.3
5.5
Ablation Study and More Exploration
Impact of explanatory text. To validate the effectiveness of our explanatory VQA text paradigm, we conduct ablation experiments comparing two variants under the same training settings: (1) LLaVA + Linear Head: Full parameter fine-tuning with linear classification head trained for binary prediction; (2) LLaVA + Explanatory Text: Training LL to generate explanatory answers instead of fixed labels. Both variants trained FakeClue, tested on LOKI; all parameters trainable. As shown Table 6 the explanatory text paradigm demonstrates advantages out-of-distribution (OOD) generalization on the LOKI benchmark.
Table 6: Ablation study comparing linear classification and explanatory paradigms.
LOKI Evaluations (ICLR 2025)
Method
Acc
F1
ROUGE L 1
CSS
LLa VA + Linear Head
81.6
77.8
LLaVA + Explanatory Text
84.3
80.1
60.9
58.2

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Performance on Real Images. In practical applications. authentic images dominate, necessitating models that can avoid misidentifying artifacts and incorrectly filtering genuine images. FilSttlsss Fake VL.'ss ..'s real image performance, showing it integrates features like object structure, lighting, color, and fine textures for authenticity assessment. This holistic approach enhances the model's reliability and robustness in distinguishing between synthetic and real images. Additional experiments, including robustness studies on image perturbations, are provided in Appendix
aus imag mearkey' retures cerrecly, with tymrettrod ean and well well-ceflannd exsiating sarkey ceter moneey's expeara nonkey speciss The tacogran and lage are che realste, with samorts
Thaiks rel rage The oates the say the baldigs iseo posts. lodielly The dbjecee the ecess with ne loaaing ansupratied etenents. acane fecioon teature stona strckarts surfect
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8
mermer.
wert
FakeVLM
FakeVLM
FakeVLM
Figure 5: Performance of Fake on real images.
6
Conclusion
The rapid growth of Al-generated images has posed challenges to the authenticity of information, driving the demand for reliable and transparent detection methods. As image synthesis detection techniques have advanced alongside large multimodal models (LMMss). approaches have shifted from non-LMMs to methods based LMMs. Our proposed VLM is large model that integrates both synthetic image detection and artifact explanation. Through an effective training strategy, Fake VLM leverages the potential of large models for synthetic detection without relying on expert classifiers. performs well in both synthetic detection and artifact explanation tasks, offering new insights and directions for future research in synthetic image detection.
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (Grant No. 625715600 62476016 and 62441617), Shanghai Artificial Intelligence Laboratory and Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing.
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Appendices
A Dataset visualization examples
6 shows some example images from the FakeClue dataset, which includes seven major cat- egories. Our dataset also contains rich variety of synthetie images with different qualities and resolutions, some which exhibit noticeable artifacts, while others are difficult to detect with the naked eye. The typical artifact characteristics vary across different categories of images, which is why we use different prompts tailored to each category in order to enhance the model's ' ability to capture these artifacts.
6
Doc
Object
Animal
Human
Scene
Satellite
Face manipulation
Real
Fake
Figure 6: Real and fake image examples from the FakeClue dataset, categorized by Document, Object, Animal, Human, Scene, Satellite, and Face Manipulation.
B Different training strategies
We explore different training strategies on FakeClue and evaluate them on both the FakeClue test set and the additional LOKI test set. As shown in Figure 7 a simple linear layer can partially activate the large model's 's capability in synthetic detection. Compared to the direct Real/Fake QA approach, the VQA format, which includes artifact explanations alongside authenticity judgments, achieves best performance. This improvement is likely due to better alignment between visual image content and textual explanations.
100
90
FakeClue LOKI
70
50
40
20
0
LLaVA
Explanatory
Figure 7: Performance of different training strategies.
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C
Robustness Study
We further evaluated the robustness of VL to common image distortions, such as JPEG compression, resizing, and Gaussian noise. Table presents the performance of model FakeClue under eight distortion scenarios: JPEG compression (quality levels 70 and 80), resizing (scaling factors of 0.5 and 0.75), Gaussian noise (variances of 5 and 10), as well additional transformations such as flipping, rotating, sharpening, and adjusting contrast or blur levels. Although the model was not explicitly trained on distorted data, FakeVLM, demonstrated resilience these low-level distortions, highlighting its robustness and practical applicability.
Table 7: Performance of FakeVLM, under different perturbations on FakeClue.
Detection
Explanation
Method
Acc 1
F1 1
ROUGE_L
CSS
JPEG 70
91.2
88.7
55.7
85.3
JPEG 80
91.0
88.5
56.3
85.9
Resize 0.5
96.4
94.9
57.0
87.0
Resize 0.75
98.1
97.4
57.7
87.5
Gaussian 10
92.1
89.8
56.1
86,4
Gaussian 5
94.5
92.5
56.5
86.6
Flip orizontal
98.4
98.3
54.0
83.3
15tate15
90.9
89.6
44.0
76.6
Sharpen 1.5
97.4
97.2
54.3
83.6
Contrast0.7
96.4
96.1
53.0
82.8
Contrast
98.2
98.0
54.0
83.3
Blur3
89.5
87.8
50.3
81.2
Origin image
98.6
98.1
58.0
87.7
D Category generalization test
In the LOKI evaluation set, different category divisions allow us to assess the model's performance across various categories. TableB[peseent the performance of VLM and GPT-4o on different image categories. The results indicate that GPT-4o exhibits noticeable category bias, whereas VLM. trained on diverse data, achieves more balanced performance across all categories. Additionally, its relatively lower performance on human portraits may be attributed to the rapid advancements in generative models within this domain.
E Performance Summary
We visualize the performance of FakeVLLM and several leading L.MS across three datasets-DD VQA 23), FakeClue, and LOKI 27 -using a radar chart, as shown in Figure X The results indicate that VLM demonstrates clear advantage existing general-purpose multimodal models in both synthetic detection and artifact explanation tasks.
F Limitations and Future Works
Despite the strong performance FLM and the scalability of the FakeClue dataset, certain limitations warrant attention. First, FakeClue's annotations are primarily derived from multi-LMM aggregation, potential biases intrinsic to the annotating models and insufficient capture of fine- grained artifacts may persist. Future iterations should incorporate heterogeneous annotation strategies, including human expert validation, to enhance dataset robustness. Second, Fake VL.M exhibits diminished sensitivity to high-fidelity synthetic images with imperceptible artifacts. Detecting such subtle inconsistencies demands more advanced methodologies capable of analyzing latent statistical irregularities beyond conventional artifact cues.

---

Table Judgment questions results of different models on the LOKI Image modality. denotes the closed-source models.
Overall
Scene
Animal
Person
Object
Medicine
Doc
Satellite
Expert (AIDE)
63.1
89.9
62.5
96.5
53.4
49.7
39.3
MiniCPM-V-2.6
44.8
52.0
34.4
53.1
31.5
53.8
51.5
38.3
Phi-3.5-Vision
52.5
50.8
41.7
71.5
34.1
57.3
54.3
60.5
LLaVA-OneVisionn-2
49.8
59.2
41.9
58.1
37.3
52.3
53.0
50.1
Intern_M--CComppeee22.
46.4
52.7
40.0
56.7
32.5
56.1
49.8
38.2
mPL.UG--WI1-37
45.9
37.3
52.9
31.4
55.3
Qwen2-VL-7B
47.8
54.7
38,9
57.9
30.3
56.0
59.6
36,9
LongVA-7B
46.2
57.6
37.4
52.5
34.1
54.4
49.8
39.7
Mantis-8B
54.6
54.9
52.2
54.8
53.5
53.1
51.9
63.3
Idefics2-8B
45.0
51.8
35.3
52.3
29.2
52.3
53.9
40.6
VL.288
49.7
58.8
39.4
54.4
37.8
53.9
60.2
44.2
Jama-3-L_ongVIIA--8
49.8
49.8
50,5
50.6
47.2
50.0
49.9
50.0
VILAI.5-13B
49.3
52.0
38.6
54.2
31.0
50.1
56.6
62.4
VL2-226
44.3
51.6
35.4
50.8
28.2
51.3
54.4
37.6
VILAI.5-40B
39.3
33.4
52.5
50.6
InternVL2-408B
49.6
55.7
37.3
59.2
34.8
55.5
64.8
40.8
Qwen2
53.2
55,9
43.4
66.9
38.0
55,9
73.7
38.2
LLaVA OIEVA-OneVistee OneVision-222
46.3
54.7
31.6
53.1
27.8
52.1
67.9
36.6
Claude-3.5-Sonnet
53.6
51.6
51.6
55.2
51.4
51.9
59.1
50.9
Gemini-1.5 -.5-Pr-
43.5
53.7
35.7
51.5
50.0
47.2
38.1
GPT
63.4
70.1
69.7
84.4
70.3
54.3
60.1
45.0
VL.
84.3
98.9
89.7
73.8
69.3
97.2
Deepseck-VL2 Deepseck VL2-small FabeVLM
Qwaa2-VL-72B InternV1224400
InterVVL2-8B Qwea2-VL-7B
GPT 4o
DD-VQA
Acc
FakeClue
CSS
F1
ROUGE L
ROUGE L
FI
CSS
Acc
Acc
F1
ROUGE_L
LOKI
Figure 8: The performances of the 7 leading LMMs on DD-VQA 23 FakeClue and LOKI 27
G Broader Impacts
This study investigates the use of Large Multimodal Models (LMMs)) for synthetic image detection and artifact interpretation, offering both significant societal benefits and potential ethical challenges. On the positive side, tools such as FakeVLM enhance the identification of Al-generated content and provide interpretable artifact explanations, thereby promoting media authenticity, digital trust, and public awareness. These capabilities critical in countering misinformation, forged visual
25

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content, and deceptive practices, with added value in user education through transparency of detection rationale.
However, the approach also raises important concerns, As generative techniques evolve in parallel with detection methods, dynamic adversarial escalation may ensue, potentially resulting in more evasive synthetic content. Moreover, false positives risk unjust censorship or reputational harm, while false negatives permit malicious media persist. Overreliance on automated systems may also weaken critical user discernment. Additionally, the computational intensity of LMMs could restrict their adoption to resource-rich institutions, exacerbating inequality in digital verification capabilities.
Thus, while the proposed framework represents a step toward securing online visual ecosystems, responsible deployment necessitates further research, ethical foresight, and inclusive public education to mitigate associated risks.
H
Label Prompt
At the end of the supplementary materials, we provide specific examples of the Label Prompts mentioned in the main text. These examples cover different categories, including Animal, Scene, Object, Human. Satellite, and Document, with variations for both real and fake labels. Additionally, we detail the specific prompt designed for the aggregation of outputs from the three initial multimodal models.
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Model output merging
You are an expert in Al-generated content analysis. Merge the following three model responses into unified answer. All responses agree the image's 's authenticity (real/fake). Prioritize explanations mentioned by at least two models and omit points unique single model. Follow these steps:
Extract Common Ground: Identify overlapping details (e.g., shadows, outlines, object alignment) across three responses.
Filter Minority Claims: Discard ohservations mentioned by only one model unless they critical (e.g., glaring artifacts).
Structure Hierarchically; Group explanations by category (e.g.. lighting, geometry, textures) for clarity.
Maintain Original Format:
Begin with "This [real/fake] image." followed by a concise,
semicolon semicolon-separated list of consolidated evidence.
Avoid Redundancy: Rephrase overlapping points to eliminate repetition while preserving technical accuracy.
Ensure Logical Consistency: If any response contains nonsensical, contradictory, or infinite loop reasoning, dardegard that portion of the answer.
Output format:
For real image:
[Explanation summarizing common realistic features, such as clear outlines, organized objects, realistic shadows, and aligned roads.]"
"This is a real image.
For fake image:
"This is fake image. [Explanation summarizing unrealistic features, such as blurry outlines, disorganized objects, unrealistic shadows, and misaligned roads.]"
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animal
Real
<image> Describes realistic areas of real animal imagery.
Note:
Animal structure and physical feature accuracy: Check if the animal's 's features like ears, paws, wings, etc., are correctly proportioned and symmetrical. Ensure the animal's body parts connected in natural and realistic manner.
Surface texture and color consistency: Examine the animal's texture is realistic, with clear, well-defined für, feathers, or skin. The texture should match natural details, and the colors should be accurate and natural, without unnatural blending over-saturation.
Object or environmental realism: Check the background and surroundings for natural elements, ensuring the animal is integrated with its environment in way that reflects the real world. The connections between objects, like water, plants, terrain, should be believable.
animal's body parts, with natural con- nections between joints, paws, or wings. The overall shape and structure should he consistent without noticeable discrepancies.
Symmetry consistency: Look symmetry
Overall color and image processing accuracy: Ensure the overall color balance the image is natural and consistent with real-world animal features, avoiding any unrealistic color shifts or over-saturation.
Output format:
Please output your answer in non-segmented text format, not Markdown format, as follows: "This is real image. [Explanation of the realistic aspects found in the image, such as accurate animal structure, natural texture, realistic environment, etc.]"
# Fake
Deseribsss unrealistic areas of synthetic animal imagery.
Note:
Animal structure and physical feature anomalies: Check if the animal's 'ss features like ears, paws, wings, ctc., are mismatched distorted. Ensure the proportions of animal parts (like paws, wings, and tails) are accurate and symmetrical. Look for unnatural connections between body parts.
Surface texture and color distortion: Examine if the animal's texture is inconsistent, such as blurry patches or unnatural blending. Ensure the fur, feathers, or skin texture matches realistic details. Look for overly saturated or unnatural colors in the animal's ''ss appearance.
Object environmental anomalies: Check the background for abnormal noise, holes, or disjointed textures. Ensure objects environment (like water, plants, or terrain) are realistically connected to the animal. Look for unnatural reflections or highlights, especially in aquatic settings.
Symmetry and consistency issues: Look asymmetry or noticeable discrepancies animal's parts. Ensure the connections between hody parts (like joints, paws, or wings) are clearly defined and consistent.
Overall color and image processing distortion: looks natural and not over-saturated. Watch color shifts that don't align with real-world animal features.
Ensure the overall color balance in the image
Output format:
Please output your answer in non-segmented text format, not Markdown format, as follows: "This is fake image. [Explanation of the issues found in the image, such as animal atructure anomalies, texture inconsistencies, unnatural envirenment, etc.]" <Few-shot Examples>
28

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scene
# Real
Simage>Descrrbee realistic areas of real animal imagery.
Note:
Physical logic accuracy: Check if all objects in the scene grounded in realistic physics, with no floating unsupported elements. Ensure that all objects placed in logical positions and follow natural laws, such as trees with visible roots furniture resting on solid surfaces.
Structural and environmental consistency: Look for harmony between the objects and their environment. Ensure that the placement of structures like houses roads makes sense within the context of the surrounding landscape. foundation, and roads should blend naturally with the terrain.
instance, a house should have a stable
Consistent lighting and shadows: Observe if the lighting and shadows align logically with one another, with clear and natural light sources. Ensure that shadows cast by objects consistent in direction and intensity, and that lighting across different parts the image (such as the foreground and background) is coherent.
Check if the colors realistic and overly saturated. Ensure that materials like grass, water, or rocks have natural, believable textures. The terrain and other elements should reflect real-world characteristics without unnatural distortions.
Natural color and texture fidelity:
Clear edges and smooth transitions: Ensure that all transitions between objects and envi- ronments are realistic and clear, without unnatural blending. For example, a shoreline should transition naturally into water, and terrain features should merge in a way that makes sense within the landscape.
Natural details and imperfections: Look for small, realistic imperfections or variations in the seene, such as asymmetry in plant growth, irregularities rock formations, or natural inconsistencies in structure shapes. Ensure that the details align with how things appear in the real world, avoiding perfect symmetry overly manufactured features.
Output format:
Please output your answer in non-segmented text format, not Markdown format, as follows: "This is real image. [Explanation of the realistic aspecta found in the image, auch as physical accuracy, consistent lighting, natural textures, etc.]"
# Fake
Demage>Describes Describes unrealistic areas of synthetic scene imagery.
Note:
Physical logic anomalies: Check if objects appear to defy natural laws, such as floating chairs trees with exposed roots. Ensure elements of the scene grounded in realistic physics, with no floating or unsupported objects. Look any objects in impossible positions or states that would not naturally occur.
Structural and environmental contradictions: Look for mismatches between the objects and their environments. For instance, check house is placed odd location. on rocky surface with no foundation, if smooth road appears in an otherwise rugged landscape. Ensure that the placement of objects makes sense within the context of the environment.
Inconsistent lighting and shadows: Observe lighting and shadows do not match, such as light sources coming from inconsistent directions, strange reflections, or broken shadows. Check if areas of the image, particularly the upper and lower portions, have lighting that doesn't 't align with one another.
Color and texture distortion: Check if colors are overly saturated or unrealistic. instance, unnatural shades of green for grass or unrealistic wave textures. Pay attention to materials that appear distorted or do not resemble real- world textures, such as block-like rocks unnatural terrain features.
Edge and transition anomalies: Look for unnatural or blurred boundaries between objects and environments. Check for smooth or illogical transitions, such as shoreline blending too smoothly into water between objects and the environment clear and realistic.
unnatural merge between terrain features. Make sure all transitions
Detail violations of reality: Examine whether dctails are too perfect or unnatural. For in- stance, overly symmetrical flower arrangements, distorted structures like leaning towers, or plants that grow in unrcalistic patterns. Ensure that all details the seene align with natural or realistic conditions.
Output format:
Please output your answer non-segmented text format, not Markdown format, follows: "This is a fake image. [Explanation of the issues found in the image, such as physical anomalies, structural contzgdictions, color distortions, etc.]"

---

object
# Real
<image>Deserries realistic of real object imagery.
Note:
Material texture authenticity: Check if the surface texture of objects reflects real-world materials accurately, such as leaves that look like real leaves or keyboard with clearly defined key textures. Ensure the textures appear clear and natural, corresponding to the material's properties.
Consistent shapes and structures: Ensure that objects have natural and realistic shapes, with no distortion or unnatural features. For instance, a keyboard should have well-defined spacehar, and boat should not be stacked on top of another in way that defies real-world physics.
Accurate color and lighting: Observe whether the colors of objects are natural and not overly saturated, and ensure that the lighting and shadows are applied in consistent, believable manner. Objects should have realistic shading based on light sources and their surroundings.
Natural color and texture fidelity: Check if the colors are realistic and not overly saturated. Ensure that materials like grass, water, or rocks have natural, believable textures. The terrain and other elements should reflect real-world characteristics without unnatural distortions.
Sharp and clear details: Ensure that details in the image, such as text on a device or edges of objects, are sharp, well-defined, and casy to interpret. There should be no blurriness loss of clarity.
Smooth transitions and connections: Check if object's transition into background or other objects is seamless and natural, without awkward or forced connections. For example, the object should appear logically integrated into its environment, with smooth connections to surrounding elements.
Output format:
Please output your in non-segmented format, Markdown format, as follows: "This is real image. [Explanation the realistic aspects found the image, such as texture authenticity, consistent shapes, accurate lighting, etc.]"
# Fake
DesageeDsseribes
Deseribes unrealistic arcas of synthetic object imagery.
Note:
Material and texture anomalies: Look for objects where the surface texture does not match reality, such leaf looks more like clay or a keyboard with distorted key textures. Ensure the textures are clear and realistic, reflecting the true material properties.
Irregular shapes and structures: Check if objects have unnatural distorted shapes, like boat stacked another boat. Ensure that the
keyboard with misshapen spacebar object align with what physically plausible.
structure and geometry
Color and lighting distortions: Watch for overly saturated colors that make objects appear unrealistic, such
overly bright unnatural color palette. Ensure that lighting and shadows are applied in consistent believable way.
Blurry and unclear details: Look for details that are fuzzy difficult to interpret, such as unreadable text electronic device unclear edges on objects. Ensure that the object's features are sharp and well-defined.
Unnatural transitions and connections: Check if the object's transition to the background other objects seems awkward unnatural, such illogical connection between the ocean and distant mountain. Ensure all object transitions and connections are smooth and realistic.
Output format:
Please output your answer in non con-seemented text format, not Markdown format, as follows: "This is fake image. [Explanation of the issues found in the image, such as texture anomalies, irregular shapes, lighting inconsistencies, etc.]"
30

---

human
Real
<image> Describes realistic areas of real human portrait imagery. Note:
Texture clarity and definition: Check if the skin, hair, and other body parts have clear, dell-defined textures, with no unnatural blending merging. Ensure the edges between areas, like fingers and hair, are sharp and distinct, reflecting world detail.
Natural proportions and symmetry: Ensure that all body parts are proportioned correctly and symmetrically, following realistic anatomical rules. For example, fingers should not be too long, eyes should be aligned, and the mouth should have natural symmetry.
Accurate color and saturation: Watch for natural skin toncs and realistic clothing colors, avoiding overly saturated or unnatural hucs. Ensure the transitions between different areas of the image, such as between skin and clothing, are smooth and true life.
Sharp details and no artifacts: Ensure that all details, such as teeth, pupils, and small facial features, are crisp and clear. There should be no noticeable artifacts, such as blurry features missing details, like poorly rendered neck.
High image quality: Check the image maintains high level clarity, with no visible noise, distortion, or blurring in the foreground or background. The image should be uniformly sharp. with consistent quality throughout.
Logical placement and accurate perspectives: Ensure that all elements within image, including clothing. hair, and body parts, are correctly positioned according to realistic per- spectives and lighting. There should be no illogical placement, such as mismatched highlights shadows on clothing.
Output format:
Please output your answer in non-segmented text format, not Markdown format, as follows: "This is real image. [Explanation the realistic aspects found in the image, such as clear textures, accurate proportions, natural color, etc.] # Fake
Simage>Descrriee unrealistic areas of synthetic human portrait imagery.
Note:
Texture blending and blurring: Look for blurred edges or unnatural fusion between differ- parts of the image, such as fingers merging hair blending into the skin. Ensure that details skin, hair, and fingers are clearly defined separated, without textures merging unnaturally.
Structural distortion and asymmetry: Check for unnatural proportions distorted body too long, an asymmetrical mouth, or eyes that are misaligned. Ensure all body parts are correctly positioned and proportioned, following realistic anatomical rules. Ensure that the structure and geometry of the object align with what is physically plausible.
parts, such finger
Color and saturation anomalies: Watch for overly saturated colors or unrealistic transitions, such as unnatural skin tones or overly vivid clothing details. Ensure the overall color scheme and texture transitions appear natural and true to life. Ensure that lighting and shadows applied in a consistent and helievable way.
Detail errors and artifacts: Look for unnatural details, such as odd textures on the teeth or irregularities in the pupils, and check for missing or incomplete details like poorly rendered necks blurry features. Ensure all details are sharp and well-defined, with noticeable artifacts or unrealistic elements.
Check if the image quality is compromised, such noticeable noise, image distortion, blurry anggroun/backggouudd Ensure the image maintains high level of clarity, with consistent level of sharpness across the image.
Overall quality issues:
Object placement and logical errors Look for any errors in the placement or logic of the image, such clothing with mismatched highlights and shadows incorrect position of elements like skirts hair. Ensure that all elements within the image align logically with realistic perspectives and lighting.
Output format:
Please output your answer in non-segmented text format, not Markdown format, as follows: "This is a fake image. [Explanation of the issues found in the image, such as texture blending, structural errors, color anomalies, etc.]
31

---

satellite
Real
<image>Descriine realistic areas of real satellite imagery.
Note:
Check if the outlines of buildings and vehicles are sharp and well-defined, without blurring or distortion.
Clear outlines of buildings and vehicles:
Organized arrangement of objects: Check objects on ground are arranged consistent and orderly fashion, with a logical spatial layout.
Natural boundary transitions: Observe if the boundaries between vegetation, land, and buildings are clear, with smooth and realistic transitions.
Realistic shadows and lighting: Check if shadows are positioned logically, following physical laws, and consistent with the light sources in the image.
Natural textures and curves: Look for textures and curves that are typical in real satellite images, with no unusual patterns or distortions.
Proper alignment roads: Check the roads are aligned and show natural curves, without any distortion, misalignment, or unrealistic interruptions.
Output format:
Please output your answer in non- segmeneet text format, not Markdown format, as follows: "This is a real image. [Explanation of the realistic aspecta found in the image, such clear outlines, organized objects, realistic shadows, aligned roads, etc.]"
Fake
Simage>Describees unrealistic areas of synthetic satellite imagery.
Note:
Blurry outlines of buildings and vehieles: Check if the outlines of buildings and vehicles are clear, especially if their edges are blurred or distorted.
Disorganized arrangement of objects: Check objects on the ground are arranged irregu- larly, appearing chaotic and lacking logical spatial layout.
Unnatural boundary transitions: Observe if the boundaries between vegetation, land, and buildings are blurred, with any lexture mixing or unnatural transitions.
Unrealistic shadows and lighting: Check if shadows appear in illogical positions, especially whether shadows of trees or buildings follow physical laws.
Abnormal textures and curves: CLook for any unnatural curves, stripes, or textures that do not match those found in real satellite images.
Distortion misalignment of roads: Check if the roads in the image are distorted misaligned, particularly road lines are clear, the direction appears natural, or there are unrcasonable curves or interruptions.
Output format:
Please output your answer in non segmented text format, not Markdown format, as follows: "This is fake image. [Explanation of the issues found in the image, such blurry outlines, disorganized objecta, unrealistic shadowa, misaligned roads, etc.]"
32

---

doc
Real
<image> realistic features real document images.
Note:
Coherent and legible text: Check the text the document is semantically coherent, logically structured, and meets standard readability, avoiding nonsensical or illogical phrases.
Well-aligned text layout: Check if text arranged a neat organized manner. mimicking the alignment and structure of genuine documents with proper margins, spacing, and indentation.
Natural fonts and typography: Observe the fonts appear consistent and realistic, without any noticeable distortions, dcformations, or irregularitics.
Consistent formatting and structure: Look for logical paragraph breaks, appropriate head- ers, and overall formatting that reflects a professionally produced document.
Attention to typographic details: Verify the clarity of characters, letter spacing, and overall text sharpness, ensuring the document appears authentic and naturally printed.
Output format:
Please output your answer in sogmeeegdnted text format, not Markdown format, as follows: "This is real image. [Explanation of realistic aspects found document, such as coherent text, well-aligned layout, natural fonts, consistent f formatting.]"
# Fake
<image> DesereDess unrealistic features of fake document images.
Note:
Incoherent and illegible text: Check if the text in the document lacks semantic coherence, contains nonsensical illogical phrases, and fails meet standard readability, making appear artificial.
Misaligned and disorganized text layout: Check if the text arrangement appears cluttered, lacks proper margins, spacing, or indentation, and does not follow the structured alignment of genuine documents.
Unnatural fonts and typography: Observe if the fonts exhibit inconsistencies, distortions, deformations, irregularities, making the document look artificial computer-generatee.
Inconsistent formatting and structure: Look for unnatural paragraph breaks, misplaced headers, or formatting anomalies that disrupt the logical flow of professionally produced document.
Verify if the characters appear unclear, have irregular letter spacing, exhibit lack of sharpness, reducing the overall authenticity of the document.
Lack of typographic detail:
Output format:
Please output your answer in non-segmented text format, not Markdown format, as follows: "This is fake image. [Explanation the unrealistic aspects found in the document, such as incoherent text, misaligned layout, unnatural fonts, inconsistent formatting.]
33

---

face manipulation
# Real
<image> You verifying KNOWN AUTHENTIC photo. Confirm:
Nostril Geometry: Natural asymmetric shape with pore-level detail.
Skin Texture: Gradual tone transitions with microscopic skin imperfections.
Eye Reflection: Physically accurate light interactions in cornea and conjunctiva.
Lip Texture: Visible lip striations with natural moisture gradient.
Shadow Integrity: Consistent ambient occlusion in nasal folds and facial contours.
Biological Signatures: Micro-movements in facial muscles and natural blink patterns.
Output format:
Please output your answer in non-segmented text format, not Markdown format, as follows: "This is real image. [Authenticity evidence]
Examples:
Please give me your analysis given picture.
ASSISTANT:
# Fake
<image>
You analyzing KNOWN SYNTHETIC image. Check these artifacts:
Nostril Area: Identify blurriness, pixelation, or Al-generated irregularities.
Skin Texture: Detect artificial color transitions, wax-like smoothing, or patchy texture.
Eye Region: Find unnatural reflections, mismatched pupil details, or Al-generated artifacts.
Lip Contours: Check for blecding colors, mismatched texture, edge inconsistencies.
Facial Contours: Identify over-smoothed jawlines, unnatural shadow transitions, or warping.
Global Consistency: Detect mismatched lighting direction, floating hairs, texture repeti-
tion.
Output format:
Please output your answer in non-segmented text format, not Markdown format, as follows: "This is fake image. [Specific technical artifacts]" Please give me your analysis of the given picture.
ASSISTANT:
34


Reviews:
[{"review_id": "nAJNy1vJYF", "reviewer": "Reviewer_q4Ca", "summary": "This study is focused on creating a diverse dataset, namely FakeClue, and use the new dataset to train a new generalized detector both synthetic and deepfake images. The new dataset is derived from a number of published datasets, like FF++, GenImage, CVUSE, ViGOR, etc with additional category labels in 7 categories, and captions that are generated by fusing Qwen2-VL, InterVL, DeepSeek captions that explain the justification of real/fake labels.  While it is generally risky to use ground truth that is strictly based on ML models which were not trained on the specific task, Section 4.1 is a good ablation study to establish that the FakeClue labels align well with human labels after the explanations. \n\nThe diversity of the dataset is particularly valuable to avoid overfitting to smaller datasets for simpler tasks, like only detecting fake faces. The model, which is based on the existing LLAVA 1.5, is trained on FakeClue seems to match or outperform the benchmarks models while being tested on the other public datasets. It is intuitive to expect that with more data, and more instructive labels with categories and explanations, the trained model performs better.\n\nOverall, this is a solid dataset paper;  the study systematically constructs a diverse datasets with categories, fake/real labels, and human understandable explanations for fake/real labels. The results show that FakeVLM model trained by the new dataset outperforms the existing SOTA models for synthetic image detection. The DeepFake detection results on FF++ also look impressive.", "questions": "1. The FakeClue dataset generates its labels combining the three major pre-trained models, Qwen2-VL, InterVL, and DeepSeek. If there are synthetic images in the dataset that are not detectable by all three, the dataset will contain incorrect labels. A detector trained by this dataset will learn to label such synthetic images as real and have a possibly bigger gap in detecting such images. How would the authors think about generalizing beyond the capabilities of these major pre-trained models?  \n\n2. An adversarial attacker may use the same three models to figure out the blind spots for all models that are trained with FakeClue. How would authors think about closing such gaps?\n\n3. The DeepFake detection results for all models are a bit dubious because most models are performing over 95% on FF++ dataset; this indicates a need for a more challenging benchmark for DeepFake detection. Evaluations with CDFv2 or DFDCP may reveal true performance differences, if there are any.\n\nCDFv2: Li , Y., Yang , X., Sun , P., Qi 370 , H., & Lyu , S. (CVPR 2020) Celeb-df: A new dataset for deepfake forensics\n\nDFDCP: Dolhansky , B., Bitton , J., Pflaum , B., Lu , J., Howes , R., Wang , M., & Ferrer , C. C. (Arxiv 2020) The deepfake detection challenge dataset", "limitations": "Detecting fake images have very important applications in real life, as explained in the paper introduction. The paper contributes a valuable dataset that can be used to improve all synthetic and deep fake detection methods.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Quality:\nThe work follows a systematic approach to generate a large and diverse dataset, namely FakeClue. The new dataset is used to train a new model, FakeVLM. The ablation studies are clear and well motivated. The results show significant improvements over the existing benchmarks. Overall, this is a solid dataset paper.\n\nClarity:\nThe paper is well written and easy to follow.  The results with the existing datasets and the new dataset allow reproduction of results. One typo in 278, DMimage dataset name seems misspelled.\n\nSignificance:\nI believe the new dataset allows all fake/real classification models to improve; this is a significant dataset contribution.\n\nOriginality:\nThis is a dataset paper; the FakeClue dataset combining a variety of artifacts, categories, multi-LMM labels of fake/real and explanations is a new dataset as explained in Table 1.", "was_revised": true, "final_justification": "The authors have answered my questions and further improved the quality of the study. I keep my accept score for this work.", "rebuttal": "We sincerely thank you for your positive evaluation and insightful questions. We are glad that you recognized the methodology of our dataset construction, the effectiveness of our FakeVLM model, and the contributions of this work. Your questions are highly valuable to us. Additionally, we thank you for your meticulous review and for pointing out the typo of \"DMimage\" in line 278. We will correct it in the final version.\nBelow, we address your questions one by one:\n\n**Q1:** Thank you for this crucial question regarding label quality and model generalization. We fully agree that the quality of the dataset's labels is fundamental to the model's performance.\n\nFirst, we would like to clarify that in our FakeClue dataset annotation pipeline, for any given image to be annotated, we first obtain its ground-truth authenticity (Real/Fake) label from the original dataset. Then, as mentioned in **Section 3.2 (lines 136-138)** of our paper, these pre-processed authenticity labels are used as prior knowledge to guide the annotation process. Specifically, for a known fake image, we use specific prompts to guide the LMM to focus on finding and describing artifacts; for a known real image, we guide the LMM to analyze its plausible and realistic features. Therefore, our data annotation pipeline, by design, ensures that known synthetic images are not incorrectly labeled as \"real.\"\n\nTo address the other aspect of your question, which concerns potential simultaneous flaws in the explanatory texts from all three models and how our trained model can surpass the pretrained capabilities of the three annotator models, our design incorporates several key points to ensure the quality and reliability of the explanations:\n- Model Diversity: We selected three top-tier open-source LMMs, each with a distinct architecture and training data. This diversity significantly reduces the probability of all three models making the same error on the same artifact in the same image.\n- Injecting Prior Knowledge: Before annotation, we provide the models with prior knowledge, including the authenticity label, image category, and common artifact types for that category. This greatly constrains the model's output and reduces the likelihood of hallucinations.\n- Reliable Aggregation Rule: Our aggregation rule explicitly requires \"prioritizing explanations mentioned by at least two models\" and \"discarding points mentioned by only a single model (unless they are critical artifacts).\" This mechanism effectively filters out random errors from individual models, ensuring the consensus and reliability of the final annotations.\n\nFurthermore, as you may refer to our response to **Reviewer vBvp**'s comment **W1**, we have experimentally demonstrated the effectiveness of the above design. Moreover, our experimental results (see **Table 2**) clearly show that the model trained on such data surpasses the original three models on both FakeClue and LOKI datasets . Finally, we completely agree on the importance of continuously improving dataset quality. In the future, we can build upon these measures by introducing manual spot-checks to further enhance the reliability of our dataset.\n\n**Q2:** Thank you again for this highly forward-thinking question. Overall, we believe that finding a universal blind spot for our model would be quite challenging. A key advantage of our FakeClue dataset is its unprecedented diversity. It covers 100,000 data points across 7 distinct categories, sourced from multiple open datasets and our own in-house data for specialized types. This diversity ensures that our model, FakeVLM, learns more general and robust artifact features, rather than flaws specific to a particular generative model or domain. Consequently, it is significantly more difficult for an attacker to find a universal blind spot that can deceive the model across all categories compared to models trained on smaller, single-task datasets.\n\nHowever, we also acknowledge that such blind spots could potentially exist. As you rightly pointed out, we believe no static dataset or model can  permanently solve the forgery detection problem. Our contribution is not just a model, but an extensible and dynamic framework. When new blind spots or generative models emerge, our data construction pipeline can be utilized to efficiently and cost-effectively integrate new content and artifact patterns into the FakeClue dataset. Subsequently, FakeVLM can be incrementally trained or retrained to continuously close these potential gaps.\n\n**Q3:** We completely agree with your assessment. The FF++ dataset indeed shows a certain saturation trend, with many models achieving very high performance. Our decision to evaluate on FF++ and the FF++-based DD-VQA was primarily because they are widely used and recognized benchmarks in the field of face forgery.\n\nTo more comprehensively test the upper limits of FakeVLM's face detection capabilities against more subtle and realistic forgeries, we have evaluated its AUC on CDFv2 and DFDC (we note that DFDCP, as per your reference, is commonly referred to as DFDC). The results are as follows:\n| |FWA [1]|Face X-ray [2]|SRM [3]|RECCE [4]|UCF [5]|FakeVLM|\n|-|-|-|-|-|-|-|\n|CDFv2|66.80|67.86|75.52|68.71|75.27|79.00|\n|DFDC|61.32|76.55|69.95|69.06|71.91|74.45|\n\nThe results show that our model achieves the best performance on CDFv2. Furthermore, on the highly challenging DFDC benchmark, which is known for its realistic settings and complex augmentations, our model also achieved a competitive AUC, demonstrating its strong forgery detection capabilities.\n\n*References:*\n\n[1] Li, Yuezun, and Siwei Lyu. \"Exposing deepfake videos by detecting face warping artifacts.\" arXiv preprint arXiv:1811.00656 (2018).\n\n[2] Li, Lingzhi, et al. \"Face X-ray for More General Face Forgery Detection, 2020 IEEE.\" CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019.\n\n[3] Lee, HyunJae, Hyo-Eun Kim, and Hyeonseob Nam. \"Srm: A style-based recalibration module for convolutional neural networks.\" Proceedings of the IEEE/CVF International conference on computer vision. 2019.\n\n[4] Cao, Junyi, et al. \"End-to-end reconstruction-classification learning for face forgery detection.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.\n\n[5] Yan, Zhiyuan, et al. \"Ucf: Uncovering common features for generalizable deepfake detection.\" Proceedings of the IEEE/CVF international conference on computer vision. 2023."}, {"review_id": "Jh2ZRGCdtu", "reviewer": "Reviewer_aBzy", "summary": "This paper proposes FakeVLM, a fully fine-tuned large multimodal model based on LLaVA-1.5-7B. By pairing a CLIP-ViT visual encoder with a Vicuna language head, the model judges the authenticity of images and provides natural-language explanations of visual artifacts. The authors also construct the FakeClue dataset, which covers seven categories—animals, humans, scenes, satellite images, and others—and contains over 100,000 real and synthetic images. Fine-grained artifact descriptions are generated through multi-model collaborative annotation and rule-based voting. Experiments show that FakeVLM outperforms strong baselines such as GPT-4o and Qwen2-VL on benchmarks including FakeClue, LOKI, DD-VQA, and DMImage, and remains robust under common distortions such as JPEG compression, resizing, and Gaussian noise. The released model weights and dataset provide a unified and interpretable framework for synthetic-image detection.", "questions": "1. What are the main differences in the annotation prompts for different categories, and how were they designed?\n2. How are the outputs of the three models aggregated? What happens when their predictions differ?\n3. Clarifying whether different categories focus on different types of artifacts and what the main differences are would also help understanding.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. This work addresses an important interpretability issue in synthetic-image detection with a simple yet effective approach, enhancing explainability.\n2. The authors release FakeClue, a new dataset with more than 100,000 annotated samples from diverse domains.\n3. The method is compared in detail across multiple datasets and models.\n4. Experimental results show that the approach is highly competitive, outperforming large general-purpose LMMs and several domain-specific expert models.\n\n**Weaknesses:**\n1. Only mild JPEG compression, resizing, and Gaussian noise are tested; additional common perturbations should be included to fully assess model stability.\n2. The process of deriving final annotations from the outputs of three models is insufficiently detailed; more specifics are needed.\n3. The paper lacks a description of how the category-specific annotation prompts were obtained.", "was_revised": true, "final_justification": "My concerns and questions have been addressed.", "rebuttal": "We sincerely thank you for your comprehensive review and positive assessment of our work. Your specific questions are very helpful for us to further clarify the details of our work.\n\n**W1:** This is a very valuable suggestion. Our robustness study in **Appendix B (Table 7)** was intended as a preliminary reference. To conduct a more comprehensive evaluation as you suggested, we have expanded this study to include more diverse and rigorous perturbations. The results for these additional perturbations are as follows:\n| |ACC|F1|ROUGE_L|CSS|\n|-|-|-|-|-|\n|flip_horizontal|98.42|98.28|54.04|83.28|\n|rotate15|90.88|89.58|43.95|76.55|\n|sharpen1.5|97.36|97.18|54.26|83.55|\n|contrast0.7|96.44|96.07|52.96|82.79|\n|contrast1.3|98.18|98.03|53.98|83.32|\n|blur3|89.50|87.76|50.32|81.15|\n|origin image|98.60|98.10|58.00|87.70|\n\nThe new perturbations include common image distortions such as rotation, flipping, sharpening, contrast adjustment, and Gaussian blur. Despite not being specifically trained on distorted data, FakeVLM demonstrates resilience to these distortions, which highlights the model's robustness and practical value.\n\n**W3&Q1&Q3:** The core difference in the annotation prompts we designed for various categories lies in the distinct points of focus and common artifact types that we predefined for each category. This design is based on prior knowledge of common generation errors for different image types, which we derived from a summary of existing literature [1] and our own empirical observations of various synthetic images.\n\nFor instance, for the Human category, the prompt guides the model to focus on details such as \"skin color transitions,\" \"eyes and eyebrows area,\" \"lips and mouth,\" and \"overall facial contours.\" In contrast, for the Satellite category, the prompt directs the model to focus on \"the clarity of building and vehicle outlines,\" \"the alignment of roads,\" and \"the realism of shadows.\"\n\nBased on this approach, we designed a total of 12 distinct prompt templates for these categories and the two authenticity labels (real/fake). We have provided detailed prompts for all categories in **Appendix G** of our paper.\n\n**W2&Q2:**\n\n**The details of our aggregation process:**  As described in **Section 3.2**, we first use three different high-performing LMMs to generate three candidate artifact descriptions for each image. Then, we employ a fourth model, Qwen2-VL, as an aggregator. This aggregator model is given the three candidate descriptions along with an instruction prompt containing detailed guidelines (see **Appendix G** for details) to produce the final, unified annotation.\n\n**Handling prediction differences:** In our pipeline, we first obtain the ground-truth authenticity label from the original dataset. As mentioned in **Section 3.2 (lines 136-138)**, these pre-processed labels serve as prior knowledge to guide the annotation. Therefore, the authenticity (real/fake) label of a known image is not subject to misjudgment by design. For cases where the artifact descriptions differ, our aggregation prompt provides explicit guidance. It instructs the aggregator model to extract common points and filter out minority opinions (unless the artifact is particularly obvious and important). In this way, we ensure that the final annotation is the result of a consensus among multiple models, making it more objective and stable.\n\n*Reference:*\n\n[1] Mathys, M., Willi, M., & Meier, R. (2024). Synthetic photography detection: A visual guidance for identifying synthetic images created by ai. arXiv preprint arXiv:2408.06398."}, {"review_id": "fWS5fppdNr", "reviewer": "Reviewer_vBvp", "summary": "This paper introduces the FakeClue dataset, comprising 100K images with fine-grained artifact annotations, designed to facilitate explanation of deepfake detection using multimodal large language models (MLLMs). Based on this dataset, the authors fine-tune LLaVA-1.5-7B and conduct a series of experiments to demonstrate its effectiveness.", "questions": "Please see weakness", "limitations": "Please see weakness", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The construction pipeline of FakeClue is well-defined and task-oriented. Integrating category-specific knowledge into prompt engineering is a good insight. And Section 3 would benefit from including additional category-specific annotation examples.\n2. The exploration of MLLMs for deepfake detection is insightful. Section 4.1 shows that using frozen MLLM features with a classification head outperforms a simple Real/Fake question-answering setting. This highlights the inherent difficulty of aligning complex visual content with binary responses, and supports the rationale for leveraging MLLMs for explanation-based detection.\n\nWeaknesses\n1. The quality of annotations generated by open-source MLLMs is questionable. Prior works such as FakeBench have demonstrated that open-source models lag significantly behind commercial LMMs (e.g., GPT-4o) in interpretability. The paper should include further visualization or discussion to support that the aggregated annotations from open-source MLLMs can achieve comparable quality to those from stronger proprietary models.\n2. The methodological novelty of the paper is limited. Section 4 largely reiterates the architecture of existing LLaVA models. While explaining deepfake detection via MLLMs is a promising direction, simply fine-tuning existing architectures on a new dataset is insufficient. The paper would be significantly strengthened by introducing task-specific modules or architectural innovations.\n3. The experimental section requires improvement. First, the set of expert baselines in Table 2 is outdated and limited. Important recent methods such as AIDE (ICLR 2025), HEIE (CVPR 2025), and C2P (AAAI 2024) should be included for a fair comparison. Second, the inclusion of FWA and CDFA in Table 5 seems irrelevant to deepfake detection—was this a mistake? Lastly, to justify the value of explanation-based tasks, the paper should compare simple Real/Fake QA against explanatory QA. \n4. The category design in FakeClue is problematic. It is unclear why “human” and “deepfake” are treated as separate categories. As an academic term, “deepfake” should not serve as a categorical label. Moreover, the appendix reveals that prompts provided for “human”, with no corresponding prompts for “deepfakes”.", "was_revised": true, "final_justification": "The authors' response has adequately addressed my concerns; therefore, I have raised my score to Borderline Accept.", "rebuttal": "**W1:** Thank you for your question. As you noted, we acknowledge that individual open-source LMMs may have a gap in explanatory capability compared to top-tier closed-source models. However, using closed-source models to annotate such a large dataset would significantly increase costs and reduce the scalability of our entire framework.\n\nRecognizing this issue, we did not rely on any single model. Instead, we developed a carefully designed multi-LMM annotation pipeline (as shown in **Figure 1**), which incorporates several measures to enhance performance. These include: designing prompts with information on common artifacts for different image categories; providing customized prompts based on the image's category and authenticity label; and, after obtaining annotations from multiple models, using a powerful model with structured instructions to extract consensus and filter noise. Through these measures, we significantly unlocked the models' potential, improving the stability and accuracy of the annotations.\n\nTo directly and quantitatively demonstrate the effectiveness of our data synthesis pipeline, we conducted an additional comparative experiment on the annotation quality of different methods. As shown in the table below, we directly compared the textual similarity scores of annotations generated by different methods  on the LOKI benchmark, of which the textual explanations are annotated by humans. We also included GPT-4V used in Fakebench and the latest versions of GPT-4o (2024-11-20) for a comprehensive comparison.\n\n| |Qwen2_vl|internvl2|Deepseek_vl2|GPT-4o (2024-08-06)|GPT-4o(2024-11-20)|GPT-4V|Qwen2_vl*|Internvl2*|Deepseek_vl2*|Aggregated_answer|\n|-|-|-|-|-|-|-|-|-|-|-|\n|ROUGE_L|0.173|0.184|0.169|0.147|0.160|0.135|0.218|0.200|0.192|0.232|\n|CSS|0.432|0.473|0.388|0.354|0.484|0.454|0.612|0.617|0.578|0.632|\n\n\\* indicates the use of our optimized prompts customized for different categories and authenticity labels.\n\nAs can be seen, while a base open-source model does lag behind top-tier closed-source models, applying our customized prompts allows the open-source model's annotation quality to surpass that of the best-performing closed-source model, GPT-4o (2024-11-20). Furthermore, the quality of annotations after aggregating the outputs from three models shows even further improvement. This strongly demonstrates the effectiveness of our design and the high quality of our annotations. \n\n**W2:** Thank you for your valuable feedback on the methodological novelty. We appreciate the opportunity to more clearly clarify the contributions of our work.\n\nUsing MLLMs for interpretable synthetic image detection is an important and relatively new research direction. While some prior works ([1][2][3]) have utilized MLLMs for synthetic image detection, they have focused primarily on the deepfake domain, leaving the task of general synthetic detection underexplored.\n\nTherefore, as described in **Section 4.1**, we first analyzed the limitations of directly applying existing MLLMs to general synthetic detection tasks. Through experiments, we discovered that although the visual features of MLLMs already contain information to distinguish between real and fake, their performance is suboptimal when simply tasked with answering a binary \"real/fake\" question. This is likely because complex visual artifacts are difficult to align with simple binary text labels. Based on this insight, we proposed and constructed the Explanatory QA training paradigm. We demonstrated through rigorous ablation studies (as shown in **Figure 3** and **Table 6**) that compelling the model to learn 'why an image is synthetic' rather than just 'if an image is synthetic' unlocks its intrinsic fine-grained visual discrimination capabilities, yielding superior performance compared to simple fine-tuning or binary classification. Therefore, although our model is primarily based on the existing LLaVA architecture, through a  combination of our high-quality data synthesis pipeline and an end-to-end Explanatory QA training paradigm, we have achieved performance that surpasses top-tier closed-source LMMs as well as SOTA expert models.\n\nFurthermore, beyond the FakeVLM model, another significant contribution of our work is the construction of the FakeClue dataset. It contains over 100,000 images across seven distinct categories, including animals, humans, scenes, satellites, and documents. More importantly, our data synthesis pipeline enables the low-cost construction and expansion of high-quality datasets, which is crucial for LMMs that inherently benefit from scaling.\n\nWe thank you again for your valuable suggestion. We agree that exploring architectural innovations, such as task-specific modules, is an effective path to further strengthen our work and enhance model performance, and we plan to conduct further explorations of the model architecture in our future work.\n\n**W3:**\n\n1. Thank you for your valuable suggestions. We have carefully reviewed the methods you mentioned, AIDE (ICLR 2025) [4], C2P (AAAI 2024) and HEIE (CVPR 2025). However, we found that the latter two methods have not publicly released their official code or model weights. Consequently, to address your concern, we have added AIDE (ICLR 2025) [4], which you mentioned, as well as two other recent and open-source SOTA models for comparison:  FreqNet (AAAI 2024) [5] and Fatformer (CVPR 2024) [6]. We have conducted a detailed performance comparison between our proposed FakeVLM and these latest methods on the FakeClue and LOKI datasets. The results are as follows:\n|Model|Fakeclue Acc|Fakeclue F1|LOKI Acc|LOKI F1|\n|-|-|-|-|-|\n|FreqNet|48.7|39.3|58.9|50.6|\n|Fatformer|54.5|45.1|58.8|48.4|\n|AIDE|59.6|86.2|71.1|82.3|\n|AIDE*|85.9|94.5|65.6|80.2|\n|FakeVLM|98.6|98.1|84.3|83.7|\n\n     \\* represents the test results of the best checkpoint trained on the FakeClue dataset according to the official default settings; otherwise, the results are from using the officially provided weights.\n\n    As the results show, our model demonstrates a significant performance advantage on both the FakeClue test set and the LOKI benchmark.\n\n2. Thank you for keenly pointing out the potential issue with the FWA and CDFA comparison methods in Table 5. We sincerely apologize for the confusion, which was entirely due to a citation error on our part. The original references [63] and [66] were incorrect. The correct citations for these methods should be:\n\n    - FWA: Li, Y., & Lyu, S. (2018). Exposing deepfake videos by detecting face warping artifacts.\n    - CDFA: Lin, Y., et al. (2024). Fake it till you make it: Curricular dynamic forgery augmentations towards general deepfake detection.\n\n    FWA is a classic method for detecting face warping artifacts, while CDFA is a recent SOTA method in general deepfake detection. We once again offer our sincere apologies for any misunderstanding caused by our citation oversight and will correct the relevant citations and descriptions in the final version to ensure the rigor and clarity of our experimental comparisons.\n\n3. We strongly agree that these two comparisons are of great importance and have already conducted this key ablation study in our paper. In **Section 4.1** and **Figure 3**, we explicitly compare four different methods, including the Real/Fake QA and Explanatory QA you mentioned. Furthermore, additional results from this four-method comparison (on both FakeClue and LOKI) can be found in **Appendix A**.\n\n**W4:** The primary reason we separated \"Human\" and \"DeepFake\" in our paper is due to their different data sources and distinct types of common artifacts. The \"DeepFake\" category in our dataset specifically refers to face forgery images from the FF++ dataset. These are primarily created by manipulating and replacing the face in a real image, so the artifacts are mainly concentrated on facial features (eyes, nose, mouth, etc.). In contrast, the \"Human\" category is sourced from datasets like GenImage, which contain fully synthetic images. These images encompass a wider variety of synthetic content featuring human subjects, and their artifact patterns may include distorted body structures or unnatural skin, which differ from classic deepfakes. \n\nWe agree that using \"deepfake\" as a categorical label is not sufficiently rigorous from an academic standpoint. Therefore, we will replace it with \"Face Manipulation\" in the final version. We will also add the missing prompt for this category to the appendix.  This prompt will guide the model to assess authenticity by analyzing key indicators. For fake images, the model will focus on detecting: (1) blurry or inconsistent face boundaries; (2) mismatches in lighting, color, or texture between the face and body; and (3) distortions in key features such as the eyes or teeth. For real images, it will emphasize: (1) smooth transitions between the face, hairline, and neck; (2) coherent lighting and shadows; and (3) clearly defined facial details.\n\n*References:*\n\n[1] Zhang, Y., Colman, B., Guo, X., Shahriyari, A., & Bharaj, G. (2024, September). Common sense reasoning for deepfake detection. In ECCV (pp. 399–415). Cham: Springer.\n\n[2] Huang, Zhengchao, et al. \"Ffaa: Multimodal large language model based explainable open-world face forgery analysis assistant.\" arXiv preprint arXiv:2408.10072 (2024).\n\n[3] Chen, Yize, et al. \"X2-dfd: A framework for explainable and extendable deepfake detection.\" arXiv preprint arXiv:2410.06126 (2024).\n\n[4] Yan, Shilin, et al. A Sanity Check for AI-generated Image Detection. In ICLR, 2025.\n\n[5] Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., & Wei, Y. (2024, March). Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning. In AAAI (Vol. 38, No. 5, pp. 5052–5060).\n\n[6] Liu, H., Tan, Z., Tan, C., Wei, Y., Wang, J., & Zhao, Y. (2024). Forgery-aware adaptive transformer for generalizable synthetic image detection. In CVPR (pp. 10770–10780)."}, {"review_id": "l4SEApsM7G", "reviewer": "Reviewer_uBJy", "summary": "This paper addresses the increasingly pressing challenge of detecting AI-generated images by introducing FakeVLM, a large multimodal model (LMM) that not only distinguishes between real and synthetic images with high accuracy but also explains the detected artifacts in natural language, thereby markedly improving interpretability. To train and evaluate the model, the authors construct FakeClue, a large-scale, multi-category dataset of more than 100 000 images. FakeClue is annotated via an innovative multi-LMM pipeline that generates fine-grained textual descriptions of synthetic artifacts. Experiments show that FakeVLM surpasses general-purpose LMMs and rivals task-specific expert models across multiple benchmarks, establishing a strong and interpretable baseline for synthetic-image forensics.", "questions": "See weaknesses.", "limitations": "Yes", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. The paper tackles the important problem of enhancing the interpretability of synthetic-image detection and articulates a clear research motivation and significance.\n\n2. The proposed method is simple yet extensible: it employs LMMs to construct an automatic annotation pipeline, enabling the creation of a large, diverse dataset.\n\n3. Extensive experiments demonstrate that the approach is competitive with both expert models and general-purpose LMMs, and a broad set of comparisons is provided.\n\nWeaknesses\n\n1. The current presentation of the FakeClue dataset lacks comprehensive visual exemplars that cover the entire spectrum of image types.\n\n2. Most experiments appear to be conducted on relatively idealized datasets, raising concerns about FakeVLM’s robustness under image degradation or other real-world distortions.\n\n3. FakeClue spans seven categories (animals, humans, scenes, satellites, documents, etc.). It is unclear whether FakeVLM performs uniformly well across all categories; category-specific biases might compromise the model’s general applicability.", "was_revised": true, "final_justification": "The authors provided detailed responses that thoroughly addressed all my concerns. I am therefore raising my rating to 6.", "rebuttal": "**W1:** Thank you for your valuable suggestion regarding the inclusion of more visual examples. We completely agree that comprehensive visualizations are crucial for demonstrating the diversity and annotation quality of our FakeClue dataset. In the current submission, we have provided some initial visual cases in **Figure 4** and **Figure 5**. According to this year's rebuttal policy, we are unable to provide additional images or links during the rebuttal stage. Therefore, in the final version of the paper, we will add more comprehensive visual cases to the appendix to showcase representative images and their corresponding artifact annotations for all seven categories in the FakeClue dataset.\n\n**W2:** This is a very important question, as a model's robustness is key to its practical value in real-world scenarios. We have already conducted a series of robustness studies in **Appendix B**, testing the model's performance against common distortions such as JPEG compression, image scaling, and Gaussian noise. The results show that even without specialized training on such distorted data, FakeVLM maintains high performance, demonstrating its resilience to common distortions.\n\nFurthermore, in our response to **Reviewer aBzy**'s **W1** comment, we tested the impact of even more perturbations on our model. The results from that expanded study also show that FakeVLM exhibits strong resilience to these distortions, further highlighting its robustness and practical value.\n\n**W3:** Thank you for your comment. We are also very concerned about whether the model performs uniformly across different categories. In **Appendix C**, we have provided a detailed category-wise performance  of FakeVLM and other models on the LOKI dataset. The results in **Table 8** show that our model, FakeVLM, achieves performance far superior to other models in almost all categories (e.g., scene, animal, object, document, satellite) and exhibits relatively balanced performance. In contrast, models like GPT-4o show a more pronounced category bias (e.g., performing well on the 'Human' category but poorly on 'Satellite'). This set of data provides strong evidence that, thanks to its training on the diverse FakeClue dataset, our model possesses stronger cross-category generalization capabilities and that the issue of bias has been well mitigated.\n\nFurthermore, to more comprehensively address your concern, we  provide an additional performance evaluation of FakeVLM on our FakeClue test set, broken down by its seven categories, as shown below:\n\n||Human|Satellite|Doc|Deepfake|Scene|Object|Animal|Overall Acc|\n|-|-|-|-|-|-|-|-|-|\n|FakeVLM|99.5|100|99.8|96.1|98.9|98.9|99.1|98.6|"}]

Author rebuttal:

Meta-review: This paper received overall positive reviews. Three reviewers (q4Ca, aBzy, uBJy) gave Accept or Strong Accept ratings, highlighting the contributions in constructing the FakeClue dataset, developing the FakeVLM model, improving interpretability, and demonstrating robustness across categories. One reviewer (vBvp) initially raised concerns about methodological novelty and baselines but was satisfied after the rebuttal and updated the rating to Borderline Accept. While the paper’s architectural novelty is somewhat limited, its systematic dataset construction, solid empirical results, and practical significance make it a strong submission. I recommend acceptance.
Decision: Accept (poster)