Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale
Authors: Zhengcen Li
Chenyang Jiang
Hang Zhao
Shiyang Zhou
Yunyang Mo
Feng Gao
Fan Yang
Qiben Shan
Shaocong Wu
Jingyong Su
Venue: ICLR 2026 Poster (2026)
Keywords: AI-Generated Video Detection
Video Generation
AIGC Detection
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Native Resolution AI Video Detection

Abstract: The rapid advancement of video generation models has enabled the creation of highly realistic synthetic media, raising significant societal concerns regarding the spread of misinformation. However, current detection methods suffer from critical limitations. They rely on preprocessing operations like fixed-resolution resizing and cropping. These operations not only discard subtle, high-frequency forgery traces but also cause spatial distortion and significant information loss. Furthermore, existing methods are often trained and evaluated on outdated datasets that fail to capture the sophistication of modern generative models. To address these challenges, we introduce a comprehensive dataset and a novel detection framework. First, we curate a large-scale dataset of over 140K videos from 15 state-of-the-art open-source and commercial generators, along with Magic Videos benchmark designed specifically for evaluating ultra-realistic synthetic content. In addition, we propose a novel detection framework built on the Qwen2.5-VL Vision Transformer, which operates natively at variable spatial resolutions and temporal durations. This native-scale approach effectively preserves the high-frequency artifacts and spatiotemporal inconsistencies typically lost during conventional preprocessing. Extensive experiments demonstrate that our method achieves superior performance across multiple benchmarks, underscoring the critical importance of native-scale processing and establishing a robust new baseline for AI-generated video detection.

Published as a conference paper at ICLR 2026
PRESERVING FORGERY ARTIFACTS: AI-GENERATED VIDEO DETECTION AT NATIVE SCALE
Zhengeen Li Chenyang Jiang' .2, Hang Zhao' Shiyang Zhou', Yunyang Mo' Feng Gao², Fan Yang Qiben Shan?, Shaocong Wu Jingyong Su
Harbin Institute of Technology, Shenzhen PPegg Cheng Laboratory PPeking University vusho@pcl.ac.. ac. cn sujiingyorg@ht..edu.com
Project Page:
-it.ee/eee..V-AAAAAAeeett....... AIGVDetection
ABSTRACT
The rapid advancement of video generation models has enabled the creation of highly realistic synthetic media, raising significant societal concerns regarding the spread misinformation. However, current detection methods suffer from critical limitations. They rely preprocessing operations like fixed-resouttion resizing and cropping. These operations not only discard subtle, high-frequency forgery traces but also cause spatial distortion and significant information loss. Further- more, existing methods are often trained and evaluated on outdated datasets that fail capture the sophistication of modern generative models. To address these challenges, we introduce comprehensive dataset and a novel detection frame- work. First, we curate a large-scale dataset of over 140K videos from 15 state-of- the-art open-source and commercial generators, along with Magic Videos bench- mark designed specifically for evaluating ultra-realistic synthetic content. In ad- dition, we propose novel detection framework built on the Qwen2.5-VL Vision Transformer, which operates natively at variable spatial resolutions and tempo- ral durations. This native-scale approach effectively preserves the high-frequency artifacts and spatiotemporal inconsistencies typically lost during conventional pre- processing. Extensive experiments demonstrate that method achieves superior performance across multiple benchmarks, underscoring the critical importance native-scale processing and establishing a robust new baseline for Al-generated video detection.
INTRODUCTION
Artificial Intelligence onteligence-Generrtted Content (AIGC) has advanced rapidly, revolutionizing the creation of high-quality text (Yang et al. 2024 DeepSeek-AI, 2024), image (Esser et al.) 2024 Labs 2024), audio (Kreuk al. 2023 Copet et al. 2023 and video (Brooks al. 2024). Among these advance- ments, video generation has seen particulart larly significant progress, evolving from foundational mod- els like Stable Diffusion (Rombach al. 2022) to more advanced architectures such as Diffusion Transformers (DiTs) (Peebles & Xie 2023 Brooks et al. 2024). as well as proprictary commercial products (Pika Labs, 2023; Jimeng AIl [024] Kuaishou 2024). These developments have pushed the boundaries of deeptake technologies (Yang et al. (022). enabling large-scale creation of fully Al-generated videos. However, the emergence near- phttooootoreaaisticc synthetic videos poses serious threats to privacy, reputation, and public trust (Wang et al. (024), underscoring the urgent need for effective detection and mitigation strategies against disinformation and misinformation.
Deepfake detection Yan et al.) 2023) and Al-generated image detection (Wang et al. 2020 Zhu ct al. 2023) have made significant progress in identifying manipulated content. However, existing deepfake detection methods (Qian et al., 2020 Xu et al.) 2023 Oorloff et al., 2024, Nguyen et al. 2024) often face generalizabillty issues as they primarily focus on detecting facial forgeries. Mean- while, approaches for detecting images generated by Generative Adversarial Networks (GAN) and
CCoresponniig author.

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Published as a conference paper at ICLR 2026
Average Appuracy (%)
85.67%
Diffusion U-Net
repvideo
wanz1
60
Cross validation Detection Accuracy
Commercial
gen
sora
76
hunyuan
68.00%
videocrafter?
STIV
pika kling
70
60
Vel on 720p
Vol. on <720p
ppensorav1
animatediff2
(In Resolution)
(Croes Resolution)
60
jimeng
Average Appuracy (%)
50
69.16%
65
40
mira
(In WwW.eelloooonorreeeeeeeee Resslution
VBench Total Score
Figure 1: Resolution mismatch and generator quality strongly affect cross-generator video detection. Left: Detectors trained on 720p videos (top) and on lower-resolution videos (<720p; bottom) both exhibit a pronounced performance drop when evaluated at spatial resolution different from that used during training. Right: We observe strong positive correlation between generator quality (VBench score) and cross- volss-vlidattinn performance (Pearson p p= 0.86), indicating that higher- quality generators tend to yield more transferable training data for detector learning. These findings motivate a unified framework that is robust to resolution shifts and generator-specific artifacts.
diffusion models (Wang et al. 2020 2023c Tan et al. 2024 Luo et al. 2024) are typically restricted static media, leaving general spatiotemporal forgery detection largely unaddressed.
Recent studies have begun to develop more robust solutions for Al-generated image and video de- tection Yann et al., 2025 Li et al. 2025; (onn et al., 2024) Chen al.. [024bb Kundu et al. 20256). A significant and shared limitation among these methods is the conventional preprocessing of resiz- ing (Yan al. 2025) cropping et al. 2025) input frames to fixed resolution (e.g., 224x224). Forgery detection methods often rely on two types of features, subtle artifacts and high-level se- mantics (Cheng et al., (025). However, this fixed-resolution preprocessing degrades both types of features. Resizing distorts the original aspect ratio, misleading detectors into learning superficial distribution differences rather than robust and generalizable forgery features (Rajan et al. 2025) Cropping, meanwhile, can discard important content outside the selected area, thereby discarding global semantic cues of high-resolution content. Furthermore, both downsampling approaches de- grade the subtle, pixel-level artifacts that are critical for identifying synthetic media and capturing fine-grained inconsistencies (Corvi et al. 2025).
Furthermore, progress in Al-generated video detection is hampered by the of outdated synthetic data sources. Existing datasets (Chen et al.) 2024b; [Song et al.] 2024) are predominantly com- posed of videos generated by earlier models, which typically exhibit low resolution, limited quality, and short durations. As a result, detection models trained on these datasets experience signifi- cant performance drop when evaluated on modern Al-generated videos. To better understand these challenges, we conduct cross-validation experiments using existing detectors on a synthetic videos dataset sourced from 14 generative models. Our preliminary results reveal critical insights, as illustrated in Figure I First, we observe significant performance drop when detectors are evaluated on videos with different resolution from those in the training set. Second, detection performance is positively correlated with the quality of the video generators, meaning that stronger detectors require training on higher-quality, more realistic synthetic videos. These findings further highlight the im- portance of constructing high-quality and diverse dataset, well training framework capable of effectively handling videos with diverse resolutions, durations and generative sources.
In response to the limitations of existing methods, propose unified framework that supports training and evaluation on videos with diverse resolutions and generative sources. First, we cu- rate a high-quality and diverse video dataset sourced from 15 representative video generation mod- els for training and develop a meticulously crafted pipeline to synthesize high-quality, human- indistinguishable videos for evaluation, termed Magic Videos. Second, we design native-resolution
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training framework based on the Qwen2.5-VL VL Vision Transformer (Bai et al. 2025) ViT), which unifies image and video modeling and enables the model to natively process videos with arbitrary spatial resolutions and temporal lengths. By removing the constraints of fixed-size downsampling preprocessing. our method achieves strong generalization capabilities to capture gen- eral spatiotemporal forgery artifacts. Extensive experiments video (Chen et al., 2024b), DVF (Song et al. 2024) and our proposed Magic Videos) demonstrate that our model is robust and achieves state-of-the-at performance in detecting Al-generated videos. Our Contributions are summarized as follows:
(Qwen2.5-
a wide range of benchmarks (Gen-
We introduce a new high-quality diverse dataset sourcing from 15 generators for training, and curate Magic Videos with recent generators for evaluation, ensuring that both training and evaluation
aligned with the current generative quality of AIGC.
We propose a novel native-resolution framework built upon the Swen2.5-ViT,, which pro- cesses videos with native aspect ratio and variable resolution, preserving crucial forgery artifacts often lost during conventional resizing or cropping.
Through extensive experiments, we demonstrate that our method achieves state-of-the-art performance and robust generalization across a wide range of benchmarks, setting new standard for Al-generated video detection.
2
RELATED WORK
2.1 VIDEO GENERATIVE MODELS
Diffusion models (Ho et al. 2020 Song et al. 2022: Rombach et al. 2022) have significantly en- hanced the quality and controllability of image generation, inspiring researchers extend these techniques to video generation tasks. Early work (Singer et al., 2022) propose incorporating motion dynamics into pre-trained text-to- text-to-image generation models. More recent studies (Chen et al.) [024a] Guo et al., 2024 Blattmann et al. 2023 Wang et al., 2023a Wei et al. 2024) leverage latent-based diffusion models (Rombach et al. 2022) to generate short dynamic videos from text or image inputs.
With the growing popularity of Diffusion Transformers (DiT') (Peebles & Xie 2023) in image gen-
cration (Labs 2024). DiT and its variants (Esser et al. 2024 have been widely proposed for video
generation tasks (Ma al. 2024b Zheng al. [024 Brooks et al. 2024 Yang al. 2025 Kong
ctt al. 2024; Wan Team] 2225 Polyak et al. 2024). Besides Diffusion based methods, Generative
Adversarial Networks (GANs) (Shen et al.] 2023 Wang et al. 2023b) are also explored for video
generation. The success of decoder-only architecture in language model has also motivated research
in generating long videos using autoregressive models (Kondratyuk et al. 2024 Yu et al., 2023 Yin
et al. 2025). Commercial video generation products (Brooks et al. [2024] Kuaishou 2024] Jimeng
A1] 2024 Pika Labs 2023 MiniMax 2024). employ complex and proprictary pipelines and pro-
duces hyper-realistic videos. However, the lack of transparency surrounding these systems limits
detailed analysis of their methodologies.
In this paper, we propose a generative video dataset that encompasses most of the aforementioned architectures, including Diffusion U-Net (Chen et al., 2024a; Guo et al. 2024), DiT (Brooks al.
2024 Wan Team 2025 Ju et al.] 2024, Zheng et al. 2024 Polyak et al. 2024 Lin et al. 2024
Ma et al. (025), MMDiT (Kong et al. 2024 Si et al. 2025). and auto-regressive (Yin et al. 2025
models. The diversity of generative models included in our dataset ensures broad coverage and supports the generalizability of the proposed method.
2.2 AI-GENERATED IMAGE AND VIDEO DETECTION
AI-Generated Image Detection. As generative technologies rapidly advance, growing number of forged images are now entirely synthesized by GANs (Goodfellow et al. (014) and Diffusion models (Rombach et al.) 2022), moving beyond traditional limited manipulation techniques. Con- sequently, substantial research efforts have focused on developing generalizable synthetic image detection methods (Tan et al. 2023 Ojha et al., 2023] Yan et al. 2024; Liu et al. (022b)). includ- ing approaches based reconstruction error (Wang al. 2023c
Luo al. [224 Guillaro al.)
2025), pixel-level features (Wang et al. 2020 Tan et al. 2024
Cheng et al. 2025), or adapting vi-
sual backbones (Koutlis & Papadopoulos
2024 Yan et al. 2025 Liu al. 2024a). These methods
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Real Videos
h:
filter
224
POLGE
480
1:
w: 224
Captioner
t:
45
w:832
Caption
w:
native resolution 3D patchification
condense
Video Generation Model
Qwen2.5-VV Vision Transformer
Classifier
Al-Generated Videas
Figure 2: Overview of the data generation pipeline and the proposed detection framework. Left: We curate high-quality captions from real videos and refine them into prompts for state- of-the-art text-to-video generators, producing realistic synthetic videos for training and evaluation. Right: Our detector supports variable spatial resolutions and temporal lengths. It avoids fixed-size resizing/cropping and applies patchification to preserve the input aspect ratio and -ne-ggaieed, high-frequency forensic cues that are often weakened by conventional downsampling. Built on the Qwen2.5-VL Vision Transformer, the framework models videos as sequences of spatiotemporal patches for robust Al-generated video detection.
typically trained images generated by specific models (Karras al. 2018 Song et al. 2022) and aim to achieve cross-architecturg generalization.
AI-Generated Video Detection. More recently, research has expanded the detection of fully
Al-generated videos (Ma et al. 2024a Ni et al. (2024] Ji et al.
2024 Chang et al. 2025). VLM-
based methods (Song et al. [024 Wen al. (025) prompt large vision- languge models identify
unnatural AI-like cues, while ViT-based methods (Chen et al. 2024b Corvi et al. 2025 intro-
duce forgery-posed generated datasets and design modules to detect spatial-temporal inconsisten- cies. However, existing methods for detecting Al-generated images and videos commonly suffer from a reliance on fixed resizing operations. Such preprocessing can lead to the loss of fine-grained details and spatial distortions, ultimately compromising model robustness across diverse inputs. In this work, we address this issue by training on native spatial resolution and temporal duration, with- resizing temporal padding. This design fundamentally avoids the pitfalls of conventional preprocessing and significantly enhances the model's generalization capability.
3 METHODOLOGGY
3.1 DATA CURATION
Selection of Data Sources. The Al-generated videos curated from multiple sources: (1) VBench (Huang et al. 2023 2024), which provides generated videos from various text-to-video models using predefined suite of diverse prompts; (2) Movie Gen (Polyak al.) 20244, which contributes videos generated by its proprietary model; and (3) A collection of highly realistic videos synthesized using various cutting-ede edge open-source and commercial models, guided by our custom- designed prompt library.
Realistic Video Generation Pipeline. To evaluate the capability of generative content detectors in real-world scenarios, we design a pipeline for constructing synthetic videos that closely resem- ble authentic content. We prioritize scenarios that pose significant risks to information security, such realistic landscapes, architectural scenes, and human interactions, particularly susceptible to misuse and misinformation due to their inherent plausibility. Leveraging ShareGPT4Video (Chen al. 2024c) repository of detailed and high-quality captions, curate
these categories
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Data
Source
Number
Resolution
Duration
Training Data
Vbench
140K
240p-768p
1-10s
(15 models)
Movie Gen
MovieGenBench
2003
1920x 1088
10.7s
Wan2.1
Open Source
450
1280x720
5.4s
Wan-I.3B
Open Source
584
832x480
5s
Hailuo
API (T2V-01)
450
1280x720
5.6s
Seaweed
API (Jimeng-S2.0)
450
1472x832
58
Seedance
API (Jimeng-S3.0)
450
1248x704
58
Vtepo
Open Source
450
950x540
8,2s
Table I: Dataset statisties for training/validationn and the Magic Videos benchmark. See ap- pendix for details of these datasets.
content specifically within these realism-oriented themes. To accommodate the capabilities of state- of-the-art architectures, filter videos by duration (3-12 seconds) and caption length (fewer than 1000 characters). The curated prompts are further optimized using GPT-4o to condense the descrip- tion under 500 characters. Table summarizes videos that are synthesized by six distinct video generators using our comprehensive prompt library. These videos represent the current frontier of photorealistic synthetic content, enabling a rigorous assessment of detection models under practical and high-risk conditions.
3.2 QWEN2.5-VIT
Contemporary Al-generated content detectors primarily operate by identifying two categories of features: local artifacts and global semantic inconsistencies (Cheng et al. 2025). However, com- mon practice in existing methodologies is to resize input images to a low, fixed resolution, typically 224x224 pixels. This downscaling operation adversely affects the features crucial for detection: degrades subtle local artifacts and distorts global semantic structures. this paper, we introduce unified framework that processes images and videos native resolution, thereby preserving the original forgery artifacts. The framework begins by tokenizing input videos into 3D patches at the native scale and adopts Qwen2.5-ViT (Bai al. 2025 forgery detection.
novel visual backbone for general video
3D Video Patchifying Native Scale. We follow the video processing steps of (Bai al. 2025, which introduces 3D patch partitioning strategy that enables native-resolution inputs. For static images, it employs standard spatial patch extraction method (e.g.. 14x14 pixels). Unlike conven- tional VITs that operate on static frames independently, our method extends patchification into the temporal dimension for video data. Given an input video tensor V R'''H x xC it partitions V to non-overlapping 3D patches of size (P:, Ph. Pw) = (2, 14, 14) and computes patch embedding via linear projection matrix E. This design eliminates the need for conventional resizing and padding operations, allowing the Transformer model to operate natively on both the spatial and temporal scales. The initial transformation of raw video tensor into sequence of feature embeddings (0) is described in Equation (I). The 3D patchification is particularly effective in detecting subtle texture artifacts and temporal inconsistencies at the patch level. By preserving the original resolu- tion during preprocessing, our method ensures that potential features critical for forgery detection remain intact and undistorted.
(0) UV P
Transformer Layer Structure. Qwen2.5-ViT consists of 32 Transformer layers, each adopt- ing pre-normalization structure, in which RMSNorm is applied before both the self-attention and feed-forward network (FFN). The FFN component employs the SwiGLU activation function. To effectively encode the spatial relationships between patches, 2D Rotary Positional Embedding (RoPE) (Su et al. 2023) is applied to the queries and keys in self-attention. enhancing the model's extrapolation capability across input resolutions. The computations performed within each Trans-
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former layer are described
X (1-1)) + Attention[RMSNommXXX""
(2)
x X0 FFNSwiGGL (RMSNorm( ( )).
In Equation 2). ((=11) and x denote the input and output hidden states of the L-th Transformer
layer, respectively.
Infrastructure Optimization for Efficiency. To address the computational challenges associated with high-resolution inputs, which typically lead quadratic complexity, several optimizations are integrated. A batch packing strategy from NaVIT (Debghani et al. (023) is adopted to allow the model to handle variable-length sequences without padding or attention masks. This combined with Flash Attention (Dao 2023), enabling GPU awareness of sequence boundaries and significantly improving both computational efficiency and memory usage through optimized CUDA kernels. In addition, hybrid attention strategy is adopted where the majority of Transformer layers utilize 114 x 11 14 windowed attention, ensuring that the computational cost scales linearly with the number of input patches.
Classifier and Tuning Methods. For the final binary classification task of distinguishing between authentic and Al-generated content, append simple yet effective classification head to the Qwen2.5-ViT backbone. The output tokens from the final Transformer layer is first aggregated into single, fixed-size feature vector using glohal average pooling. This vector is then passed through single fully connected (FC) linear layer that outputs the logits corresponding to the "real" and "gen- erated" classes. To adapt the pre-trained model this task, we explore three fine-tuning strategies: (1) Full Finetuning: Both the visual backbone and classification head are jointly optimized during training. (2) Linear-Probing: Serves as a baseline, where the entire vision backbone frozen and only the classification head is trained. (3) Parameter-Efficien Fine-Tuning (PEFT): Specifically, adopt Low- Low-Rank Adaptation (LoRA (Hu al. 2021)), which introduces small, trainable low-rank matrices into the frozen backbone, allowing only subset of parameters to be updated.
4 EXPERIMENTS
4.1 DATASETS
Training Dataset. We construct training set of 70K Al- Al-generated videos and 70K real videos.
The synthetic videos are generated by VBench (Huang et al. 2023) using their prompt set, while the real ones are sampled from MSVD (Chen Dolan 201 and Kinetics (Kay al. 2017). For
validation, we use 1,003 fake videos from MovieGen VideoBench (Polyak et al. 2024) and 1,000 real videos from Panda-70M (Chen et al. 2024d).
Model
Training Data
Movic Gen
Wan 2.1
Wan- 1.3B
Hailuo
Seaweed
Seedance
StepVidee
mACC
RINE
Idm
52.97
45.35
33.56
51.4
$1.63
44.65
70.23
49.47
FatFormet
ProGAN
50.02
50.00
50.17
50.00
50.00
50.00
50.23
50.07
B B-Freet
S.1.1
64.30
53.72
68.32
55.81
31.63
36.05
48.60
49.02
Effortt
1.4
70.74
73.26
66.95
67.44
86.05
83.26
62.79
73.29
WaveRept
Pyramid Flow
65.30
58.84
53.6
59.53
39,53
57.21
59.53
58.04
F3Net
92.51
70.64
TALL
91.71
64.65
67.81
61 86
64,42
63.49
63.49
64.29
TimeSformer
91.41
68. 84
72.09
66.28
69.77
67.91
67.21
68.68
15Model-140K
CLIP VIT-L/14
(Ours)
99.20
78.14
77.74
77.21
77.91
77.21
75.58
77.30
X-CLIP-B/16
98.55
76.28
72.43
75.12
75.12
75.35
72.33
74.44
X-CLIP-L/14
98.85
80.00
86.13
79.53
78.60
80.00
79.53
80.63
Moon-ViT
98.25
76.74
79.62
75.81
76,74
75.81
74.88
76.60
Qwen2.5 5-VT (Ours)
97.20
85.81
83.39
84.65
83.95
84.88
76.51
83.20
Table 2: Accuracy (ACC) Benchmarking Performance on on Movie Gen (val) and Magic Videos (test), reported per generator and averaged (mACC). i Results are produced with the official pretrained model. Best: bold; second best: underlined.

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Model
Training Data
Movic Gen
Wan 2.1
Wan. 1.3B
Hailuo
Seaweed
Scedance
SlepVides
mAP
Idm ProGAN
32.69
46.00
FatFormett
58.84
39.06
54.96
43.80
44.55
42. 16
52.36
46.15
B-Frest
SD 2.1
70.38
59.80
73.81
62.62
38.93
52.37
53.94
Eflont
SD 1,4
80.60
76.61
73.75
70.67
95.48
91.65
63.41
78.60
WaveRept
Pyramid Flow
92.97
83,50
85.31
95.
99.12
78.64
94.04
89.38
F3Net
96.20
85.79
78.05
80.31
92.13
87.32
81.26
84,14
TALL
96.07
83.59
80.78
78.58
88. 81
82.81
83.39
82.99
NPR
82.70
88.82
TimeSformes
15Mrdel- (Ours)
96.91
82.44
83.04
75.42
83.49
80.29
78.55
80.54
CLIP ViT-L/14
X- CLIP B/16
99.87
94.94
94.07
95.76
87.65
97.78
81.65
91.98
X-CLIP--/114
82.73
96.
Moon-ViT
99.24
93.84
90.39
87.24
94.11
90.68
81.69
89.00 06
Qwen2.5-VII VIT (Ours)
99.46
96.67
99.68
94.20
91.59
96.92
80.63
93.28
Table Average Precision (AP) Benchmarking Performance on on Movie Gen (val) and Magic Videos (test), reported per generator and averaged (mACC). i Results are produced with the official pretrained model. Best: bold; second best: underlined.
Test Datasets. To evaluate robustness against state-of of-----rttttttt- synthetic videos, introduce the Magic Videos benchmark (Table [ which consists of high-quality, hyper-realistic vidcos gener- ated by six cutting- gutting-edge video generation models using carefully curated prompts. Each generated video subset is paired with corresponding real videos to support binary classification evaluation. We report performance using Accuracy (ACC) and Average Precision (AP) per generator subset. Ex- ternal Benchmarks: In addition, we evaluate our method on the test sets of three external datasets: DVF (Song al. 2024), Vedeo (Chen et 2024b), and DeepTraceReward (Fu et al. 2025). These datasets encompass diverse real-worke sources and wide range of video generation mod- els, enabling more comprehensive assessment of detector performance across different synthesis techniques and generation stages.
Baselines. We benchmark four categories of methods: (1) Al-generated video detection ap- proaches (MM-Det (Song et al. 20256), TruthLens (Kundu et al. 2025a). and WaveRep (Corvi et al. 2025)); (2) visual and video foundation backbones (X-CLIP-B/16 (Ni et al., 2022), X-CLIPPLL/14 (Ni et al. 2022 TimeS- former (Bertasius et al. 2021). 1) and Moon-ViT (Du et al. 2025)): )) (3) deepfake detection methods (TALL (Xu et al. (023) and F3Net (Qian et al. 2020)): ; and (4) general Al-generated image detec- tion methods (NPR Tann et al. 2024), FatFormer (Liu et al. 2024a). RINE (Koutlis & Papadopoulos
2024), DeMamba (Chen et al., 2024b), UNITE (Kundu et al.]
2024), B-Free (Gullaro laro et al., 2025), and Effort (Yan et al. 2025 ). For image-based methods, we
report the results by averaging logits over T frames to obtain video-level predictions.
Implementation Details. We train our model for five epochs using the binary cross-entropy loss and the AdamW optimizer. The learning rate is set to x 10 for full fine-tuning and 1 10-4 for parameter-efficiett fine-tuning (PEFT). To balance performance and computational cost, adopt the preprocessing strategy described in Bai et al. (2025): ) Du et al. (2025), which specifies minimum and maximum token budgets for image inputs. Each input frame is resized the highest possible resolution within the mnin-pixels, max-pixels 1) range while preserving the original aspect ratio. In our experiments, we consider resolution ranges per frame: (224 224, 720 x 720) and (224 x 224, 448 x 448). For temporal sampling, videos are decoded into frames at 2 fps. During training. we randomly sample
consecutive frames; during evaluation, we instead select the central T 8 frames. Additional implementation details are provided in the Appendix.
4.2 AI-GENERATED VIDEO DETECTION
Evaluation on Magic Videos. The experimental results presented in Table and Table provide a comprehensive evaluation of our model against several distinct classes of methods. notable observation is the underwhelming performance of models originally developed for Al-generated image detection, including RINE, FatFormer, B-Free, and Effort. These models exhibit relatively poor performance trained on our video datasets. This discrepancy suggests a fundamental difference between forgery patterns present in static images and those in dynamic video sequences; features learned for detect-
video-based benchmarks, even compared to image-based based methods that
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Video-
Zero-
Open- Sora
Stable Stable Video
Method
Sora
Pika
AVG
Crafter scope
Diff.
CNNDet
87.4
88.2
78.0
63.8
77.3
73.5
78.9
78.2
DIRE"
55.9
61.8
53.8
60.5
65.8
62.7
69.9
62.1
MM-Det
93.5
94.0
88.8
86.2
95.9
95.7
89.9
92.0
NPR
86.6
85.6
96.0
81.0
94.6
71.1
97.0
87.4
TALL
95,4
91.8
97.2
94.9
97.5
83.6
98.2
92.6
F3Ner
90.4
90.2
95.9
90.1
97.8
93.1
98.5
93.7
TimeSformer
94.5
92.7
98.0
92.5
98.4
92.4
99.5
95.4
Qwen2.5-Vin (Ours)
93.5
99.8
98.6
96.4
99.1
95.6
99.7
97.6
Table Benchmarking Results in terms of AUC Performance DVF-Test Song et al. (2024).
Results with are derived from Song et al. (2024).
Averuged
Overall Recall
Fake ACC
Real
Model
Recall
Fl
AP
ACC
Method
ACC
ACC
UNITE
89.60
92.76
GPT-5
90.7
84.6
98.8
TruthLens
90.49
GPT-4.1
92.9
89.1
97.9
DeMamba-CLiP
91.58
89.19
93.45
96.14
92.29
Gemini 2.5 Pro
84.3
75.7
95.8
NPR
83.01
47.99
63.66
86.75
92.40
VideoLLaMM3 7B
10.0
38.1
4.3
F3Net
83.48
56.78
71.57
88.26
93.06
Qwen2.5-VL.
51.7
20.2
93.4
TimeSforner
86.42
65.38
77.67
87.51
91.55
Qwen2.5-VL.32
47.4
8.9
98.5
TALL
89.44
61.51
76.67
90.05
91.76
Qwen2.5.VL 72B
50.0
16.6
94.3
CLIPA.
87.72
64.73
77.62
92.64
90.57
DeepTraceReward
XCLIP-B
89.79
53.76
72.04
92.60
74.7
55.7
100.0
XCLIP-L
88.94
61.60
78.84
92.74
92.43
(w/ Qwen2.5 7B)
Qwen2.5-ViT VIT (Ours)
91.16
90.64
96.13
96.64
93.18
Qwen2.5- VIT (Ours)
97.2
96.3
98.2
Table 5: Benchmarking Results in terms of averaged Table 6: Benchmarking Results in terms Recall, FI, AP per subset and overall Recall and of ACC on DeepTraceReward (Fu et al., ACC Performance Genvideo-Val Val (Chen et al., 2025). Results of baseline methods are re- 2024b). Detailed results are Appendix.
ported (Fu et al. 2025).
ing image artifacts do not generalize well to the spatio-temporal domain required for video-level analysis. Similarly, methods designed specifically for deepfake detection, such as F3Net and TALL demonstrate limited effectiveness. While these models excel at identifying at facial manipulations, their specialization becomes a constraint when faced with the broader challenge of detecting fully synthesized videos. In contrast, pretrained visual backbones like TimeSformer, CLIP-ViT and X- CLIP exhibit competitive performance by leveraging extensive pre-training on diverse visual data. However, their effectiveness is ultimately constrained by architectural limitations. A primary issue the conventional practice resizing input frames to a fixed resolution of 224x224 pixels. This downsampling process eliminate subtle forgery artifacts and disrupt global semantic features crucial for detecting sophisticated generative content. Moon-ViT (Du et al. 2025), which applies a similar processing pipeline based on Na also suffers from this limitation as operates static images and cannot capture temporal inconsistencies. Our proposed method achieves the highest average scores in both ACC and AP, establishing a new state-of-the-art these benchmarks. Our proposed method achieves the highest average accuracy (mACC) and highly competitive average precision (mAP), establishing a strong baseline. While our model does not yield the absolute best AP on every individual generator, it consistently delivers strong and balanced performance across all generator types, highlighting its exceptional generalizability. This superior performance di- rectly attributed to our advanced architecture. By leveraging the Qwen2.5- VIT backbone, our model integrates native-resolution modeling with dynamic temporal duration modeling, thereby avoiding destructive downsampling and preserving the fidelity of forgery cues inherent in the original con- tent. By effectively capturing both fine-grained artifacts and high-level semantic inconsistencies. model provides a more robust and accurate solution for detecting Al-generated videos.
Evaluation on DtF-test. We use the same model weights trained on our 15model-14pp dataset to directly perform cross-dataset evaluation on the DVF dataset (Song et al. 2024). The results
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Published as a conference paper at ICLR 2026
Archs.
Variants
Magic
Genvideo
Avg.
random crop to 224p
62.62
93.50
78,06
spatial resolution
random resize to 224p
73.69
95.52
84.61
dynamic [224p, 448p]
81.19
96.01
88.60
dynamic [224p, 720p]
83.20
96.64
89.92
T=2
71.15
94.70
82.93
temporal
T=4
75.58
94,40
84.99
resolution
T=8
81.19
96.01
88.60
LP
70.60
91.91
81.26
tuning mode
LoRA(r=16)
78.73
94.95
86.84
full
81.19
96.01
88.60
Table 7: Ablation studies regarding spatial-temporal resolution and tuning mode. We report averaged ACC(%) on Magic Videos and Genvideo. For temporal and tuning experiments, the spatial resolution is set to dynamic(224p. 448p].
presented in Table Our model achieves the highest average AUC of 97.6. demonstrating the high quality of our training dataset and the strong generalizability of our model in detecting Al-generated videos across diverse generation techniques.
Evaluation on Gen Video- Val. We directly evaluate the model trained on the 15model- dataset on Video-Vallll (Chen al. 2024b) using the same weights. Owing to the substantial class imbalance between real and generated samples in the Gen Video evaluation subsets, we report both overall recall and accuracy (ACC) for more comprehensive comparison. As shown in Table our method outperforms all baselines, including UNITE (Kundu et al. 2025b), which is specifically designed for Al-generated video detection, well larger MI.LM-based TruthLeens (Kundu al., 2025a) and other baseline approaches trained on the same data as ours. Notably, despite using only one-fifteenth of the training data used by DeMamba (Chen al. 2024b), our model achieves bet- ter performance and strong cross-dataset generalization, particularly on videos generated by earlier models.
Evaluation on Deep Rewad. To further demonstrate robustness against unseen generators, evaluated method the Reward (Fu et al., 2025). which contains 4,335 videos from recent generators (including Pika-1.5, Kling- I.5, etc). Table compares our Qwen2.5- ViT against leading multimodal LLMs. Our model achieves 97.2% accuracy, significantly outperform- ing large general-purpose VLMSs (e.g., GPT-5, Gemini 2.5 Pro) on the binary classification task. Moreover, while general-purpose VL.Ms often struggle with detecting synthetic video, model demonstrates balanced performance (96.3% Fake ACC vs. 98.2% Real ACC), proving its effective- ness in identifying artifacts from the latest generation engines without overfitting to specific training generators.
4.3 ABLATION STUDY AND ANALYSIS
We conduct series of ablation studies, as detailed in Table to systematically investigate the im- pact of spatial resolution, temporal resolution, and different fine-tuning strategies on our model's performance. We observe that the benefits of high-fidelity inputs are substantially larger on Magic. In contrast, Gen Video contains lower-resolution videos with shorter durations; therefore, it is less sensitive to the performance degradation introduced by aggressive downsampling during prepro- cessing. As result, improvements brought by higher spatial/temporal fidelity are more pronounced on Magic than on Video.
Ablation Study on Spatial Resolution. Our analysis reveals critical performance differences. The conventional random crop to 224p method yields the lowest average accuracy on high-resolution content. Switching to random resize to 224p boosts performance to 73.69, but this approach still cause degradation of subtle artifacts. In contrast, our dynamic resolution strategy which preserves the original aspect ratio demonstrates markedly superior performance, with the overall average

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Published as a conference paper at ICLR 2026
100.0
100
100
100.0
Relative ACC (%6
97.5
97.5
90-
95.0
95.0
92.5
Ours
85
00
75
70
87.5
XCLIP-L
85.0
60
85.0
JPEG Quality
H264 CRF
Resize Scale Factor
Crop Factoo
Figure 3: Robustness on MovieGen under compression and spatial perturbations (relative ACC). Perturbation methods include JPEG compression, H264 encoding, spatial resizing and crop- ping.
curacy peaking at 89.92 when using resolutions up to 720p. This confirms our hypothesis that main- laining aspect ratio and processing at higher resolutions are critical for capturing subtle, pixel-level forgery artifacts.
Ablation Study Temporal Resolution. For all candidates, sample the original videos 2 fps and select random or center-aligned frames during training and testing, respectively. We observe that incorporating more temporal context is beneficial. Increasing the number of sampled frames (T) from 2 to 8 improves the average performance from 82.93 to 88.60. This suggests that longer sequences enhance the model's ability detect temporal inconsistencies common in Al-generated videos.
Ablation Study on Tuning method. Regarding tuning strategies, full fine-tuning achieves the best average performance (88.60) 60). Although the parameter-efficient LoRA approach significantly out- performs linear probing, full fine-tuning is justified for maximizing detection accuracy.
Robustness Analysis. We evaluate our model's robustness under common video perturbations, in- cluding compression, downscaling, and cropping, as shown in Figure The model remains highly accurate under mild degradations such as moderate JPEG and H.264 compression. Performance drops become more pronounced with severe spatial changes. Notably, our model outperforms base- lines under aggressive downscaling (scale 0.4) and cropping (crop factor 2 0.15), though all methods are affected by extreme spatial loss. These results highlight strong robustness to moderate noise and sensitivity to substantial spatial degradation.
5 CONCLUSION
In this work, we tackle two critical limitations in current AIGC detection methodologies: the re- liance on outdated training data and the prevalent of destructive fixed-resolution preprocess- ing. Our contributions are twofold. First, we introduce a comprehensive and up-to-date dataset comprising videos generated by 18 state-of-the-art generators, ensuring broader coverage con- temporary synthesis techniques. Second, we propose a novel detection framework capable of op- erating directly on videos dynamic spatial-temporal resolutions. By leveraging the Qwen2.5-VL. VIT backbone, method avoids the information loss associated with downsampling, successfully preserving both fine-grained forgery artifacts and high-level semantic inconsistencies. Extensive evaluations demonstrate that our approach achieves state-of-the-art performance and substantially improves cross goossreen robustness and generalization.
Limitations. Despite our efforts to construct a comprehensive dataset, the rapid evolution of gen- crative models poses an ongoing challenge. Continuous data updates will be necessary to keep pace with emerging architectures. Additionally, processing videos their native resolution inevitably incurs higher computational costs compared to downscaling- ployment in coouure-coonstrinne environments. Future work will focus improving the compu- tational efficiency of native-scale processing and investigating model explainability to deepen our understanding of the specific forensic traces leveraged for detection.
methods, which may limit de-
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Published as a conference paper at ICLR 2026
6 ACKNOWLEDGMENT
This work was supported by the project Peng Cheng Laboratory (PCI_205AA1)), the National Natural Science Foundation of China (Grant No. 62350710797), and the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2023B1515120065)
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APPENDIX
This appendix provides a detailed analysis of our dataset, implementation details, additional experi- mental results, and visualizations:
Section A: Data distribution and analysis of our dataset.
Section B: Cross-validation experiment.
Section C: Additional implementation details for both our method and baseline meth-
ods.
Section D: Additional experimental results and ablation studies.
Section E: Visualizations and discussion.
A DATASET COMPOSITION
Model / Video Source
Ver.
Availability
Videos
Resolution
FPS
Frame
Duration
Real Kinetics-400 Kay al.
2017
17.05
real videos
70K
144p-720p
5-10s
VedVideo Si et al.
2025
25.01
open-source
4720
720x480
8
49
6.1s
Wan2.1 Wan Team 2025
25.01
open-source
4725
1280x720
16
5.0s
CausVid (5x) Yin et al. 2025
25.01
open-source
4720
640x352
24
120
5.0s
Apple-STIV Lin ci al. 2024
24.12
open-report
4715
512x512
60
Sora (Brooks al. 2024
24.12
private
4720
854x480
30
5.0s
Hunyuan Video (Kone al 2024
24.12
open-source
4725
1280x720
24
5.
Gen-3 GGermandrss 2244
24.06
private
4707
1280x768
24
10.7s
Luma Lumalabs
24.06
private
4680
1360x752
24
5.0s
Kling Kuaishou 2024
24.06
private
4679
1280x720
30
5.1s
Jimeng (Jimeng ATl 2124
24.05
private
6214
1280x720
8
12.08
OpenSora VI.I (Zieng
2024
24.04
open-source
4720
424x240
8
8.0s
Mira (Ju al. 2024
24.04
open-source
4721
384x240
6
10.0s
VideoCratter-ZZu Chen (Chen et
2024a
24.01
open-source
4720
320x512
10
1.68
Pika 1.0 (Pika Labs [225
23.11
private
4715
1280x720
24
3.0s
AnimateDitt-vZ (Guo et al.
2024
23.09
open-source
4715
$12x512
8
16
2.08
Overall Fake
70,692
240-720p
6-60
16-256
1-12s
Table 8: Statisties of real and synthetic videos in the proposed training set.
Model/ Video
Split
Videos
Resolution
FPS
Frame
Duration
Movie Gen PPolykk al. 2024
validation (fake)
1003
1920x 1088
24
256
10.7s
Panda-70M Chen et
2024d
validation (real)
1000
720p
6-30
10-50s
Mixkit mixkit 2024
215
720p
15-60
10-175
test (real)
Pexcls
pexels
2024
292
720p
24-60
6-39s
Wan2.1 1 Wan Team 2025
215
1280x720
30
161
5.4s
Wan-1.3B ( Wan Team 2025
292
832x480
16
81
5.0s
Hailuo
MiniMax 2024
2155
1280x720
25
141
5.6s
test (fake)
Seaweed (Saawead
2025
2155
1472x832
24
121
5.0s
Seedance Gao et al. 2025
215
1248x704
24
5.0s
StepVideo (Ma el al. 2025
215
960x540
25
204
8.2s
Table 9; Statistics of real and synthetic videos in the proposed validation and Magic Videos Benchmark.
A.1 TRAINING SET.
Table provides a comprehensive summary of the training dataset used our work. Previous research has emphasized the critical importance of dataset quality and diversity in training robust detectors (Rajan et al. 2025), especially given the variety of artifacts produced by different gener- ative models (Wu al. 2025). To advance the field of Al-generated video detection, curated
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large-scale dataset comprising outputs from 15 distinct video generation models. The majority of these synthetic videos are sourced from VBench (Huang et al. 2023). a benchmark selected for its models. This choice allowed us to avoid the costly and time-consuming processes of large-scale video filterin,, quality control, and generation while ensuring high quality and consistency of generated video data.
high-quality prompt library and extensive evaluation of sfate-of-the
Our dataset reflects the diverse and evolving landscape of video generation, featuring models de- veloped between 2023 and 2025. It includes a wide range of model types in terms of availabil- ity (i.e., open-source, open-report, and private) and architecture (e.g., Diffusion U-Net, DiT-based, auto-regressive models, and others with undisclosed architectures). The models differ significantly in training methodology, data scale, output resolution, and video duration, contributing to a richly diverse training set.
To complement the synthetic videos, we sampled an equal number of real videos from Kinetics- 400 (Kay et al., (017). These were carefully selected match the resolution, duration, and encoder distribution of the generated videos. This matching is essential for reducing potential biases and ensuring that the learned features are genuinely discriminative between real and fake content.
A key feature of our dataset is that all generative models were conditioned on the same prompt library, ensuring a shared semantic distribution across the generated videos. This unique setup enables controlled cross- cross-validation experiments, allowing to investigate inter-model relationships and identify key factors that influence detector performance, as discussed in Section B.
A.2 VALIDATION AND TEST DATA
Table 9 presents the composition of our validation set and introduces a novel, high-quality Magic Videos Benchmark, which we name the Magic Videos Benchmark.
Validation Set. Rather than adopting the common practice of partitioning a subset of the train- ing data, we constructed the validation set from videos generated by Movic Gen (Polyak et al, 2024), model that is architecturally and semantically similar but not identical to the models used in training. These synthetic videos paired with 1,000 of real videos sampled from the Panda- 70M (Chen et al.] 2024d) dataset. During training. we apply early stopping based on the validation loss computed on this set. This strategy helps mitigate overfitting to the specific models and sce- narios encountered during training, promoting the selection of a model checkpoint with stronger generalization capabilities.
Magic Videos Benchmark. We identified critical gap in existing benchmarks: they often lack coverage of the latest generative models and may exhibit evaluation biases. To address this, we constructed the Magic Videos Benchmark using high-quality video generation pipeline, as intro- duced in Section 3 of the main paper. This benchmark includes real videos from two premium Maaffooms-Mixiiqimiiiie
(024) and Pexels(pexels -0veincovering diverse range of common scenes such as landscapes, architecture, human subjects, and news footage. These videos are pro- vided at resolutions up 1080p to ensure both high fidelity and content diversity. For evaluation, real and generated videos are matched into balanced subsets, allowing for the computation of accu- racy and other performance metrics.
To generate the synthetic counterparts, we first applied ShareGPT4Video (Chen et al. 2024c) to pro- duce high-quality captions for the real videos. These captions were then refined through rigorous process of filtering, rewriting, and final prompt polishing. The resulting prompts were input to six advanced text-to-video models, comprising both open-source and commercial systems. Below, we detail the generative models used to construct the Magic Videos Benchmark:
Wan2.1 (Wan Team 2025): We used the Wanxiang platform API with the "professional" model, default settings, and prompt optimization disabled. Prompts were derived from the Mixkit collection. This model may apply post-processing, resulting in a higher frame rate than 14n-14B.
Wan-1.3B (Wan Team 2025): Videos were generated using the official open open-source source imple- mentation and pre-trained model, with prompts from the Pexels collection.
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Hailuo MMiniMax 2024): Accessed via the MiniMax-T2V-01 commercial API, this model was configured to generate 5-second videos using prompts from the Mixkit collection. Prompt optimization not applied.
Seaweed(Seawead et al. 2025): As official model weights are not publicly available, we used the commercial model Smmeng-S2.00iimn ATl 2024), which is based on the Seawecd-alpha model. Prompts
sourced from the Mixkit collection. Generation was performed using prompts from the Mixkit collection.
Seedance Gao al. 2025); In place of unavailable official weights, we used the commer- cial model Simmeng-S3.00immnn
All (2024), corresponding to the Seedance 1.0 Mini model. Prompts were sourced from the Mixkit collection.
StepVidee (Ma et al., 2025): Videos were generated using the official API with the Step- Video-T2V endpoint (544px x 992px x 204f), using prompts from the Mixkit collection.
B
EXPERIMENT
B. EXPERIMENT SETUP
VolsdValii Setup. This experiment focuses on in-domain, cross-model validation of detec- tors. The benchmark utilizes data generated by 15 models from VBench (Huang et al. 2002)) which evaluates various generative models using shared set of predefined prompts. Because all mod- els generate videos from the same prompt library, consider their outputs belong to the same semantic domain. Let F; denote the subset of videos generated by model i, and let Ro represent fixed set of real videos, sampled to contain the same number of examples as each F, For each model i, we train a deepfake detector on the dataset F,, Rn and evaluate its performance on all other generated subsets F, (for + i). This setup allows of detectors across different generative architectures while keeping the semantic domain fixed. It also provides a controlled environment for analyzing the relationships between generative model ar- chitectures and detection performance. This Cross- Validatio Benchmark produces an n matrix M. where Míi, j] represents the recall of a detection model trained on subset and evaluated on subset Based on preliminary observations, we propose the following two hypotheses, which will be validated in subsequent experiments.
rigorously assess the generalization ability
Similarity Between Generative Models. The matrix entry Mii,j] reflects the output similarity between generative models i and j. influenced by factors such as model architecture, sampling strategies, and training data. We observe that models with more similar architectures tend exhibit higher cross-validation accuracy between them. To quantify this relationship. we define a non- directional distance metric. d(i.j) 1 0.5 (M + MM.. i)). Using this metric, we apply Non-metric Multidimensional Scaling (MDS) (Kruskal) 1964) to produce a 2D spatial representation of the generative models. This visualization aids in understanding the architectural relationships and clustering patterns among the models, offering insights into how architectural similarity correlates with cross-detection performance.
Impact of Generation Quality. In addition to architecture, M [i.j]] is also influenced by the gen- cration quality of model We hypothesize that higher-quality synthetic vidcos provide more re- alistic and informative supervision signals, enabling the classifier learn more effective forgery- discriminative features. Since ground-tre -oundd-truth quality labels are unavailable, we adopt scores from proxy for
recent T2V benchmarks (Huang al., 2023 Liu al.) [202cc] (Huan al., (2024) generation quality. To assess the relationship between generation quality and detection effective- ness, we compute Pearson correlation coefficients (p) between the benchmark quality scores and corresponding detection accuracies.
B.2 VALSS-VALIAAIOON RESULTS
Cross Validation. As discussed above, we use the cross- cross-validation matrix M to evaluate the similarity between generative models. Four detection models F3Net et al. (020), X-CLIP- B/32 et al. 2022). TALL(Xu et al.] 2023), and NPR Tan et al., (024) are trained on 5K real videos from MSR-VTT and 5K generated videos from each specific model subset. These detectors
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mira
MMDIT
Autoregressive
Diffusion U-Net
DIT
Commercial
animatediff2 videocrafter2
STIV
luma
sora
hunyuan
wan21
opensorav1-1
pika
gen3
kling
repvideo
jimeng
causvid
Figure 4: MDS visualization of generator similarity induced by cross-model detection perfor- mance.. Model similarity is based on pairwise detection accuracy.
Wan2
Hun- yuan
Kling
Gen-3
Rep- Video
Jimeng TeonngTun Luma
Open Sora
Caus Vid
VCrff ter-2
ADiff
-V2
44.7
Hunyuan
93.9
99.4
81.3
92.9
92.0
69.7
89.7
91.4
38.5
84.4
34.2
74.2
89.8
86.3
90.6
80.35
Kling
76.7
Sora
90.3
84.9
75.6
99.5
93.7
78.7
99.2
96.1
35.2
87.1
34.1
70.7
98.0
88.3
90.4
81.44
Gen-3
RepVidoo
97.0
91.5
92.8
96.9
98.3
98.8
98.6
97.8
29.4
94.8
56.6
86.9
99.8
91.7
91.1
88.13
Jimeng"
61.1
47.9
39.9
78.4
81.6
54.5
99.7
81.3
14.8
70.9
26.4
39.6
$5.0
60.23
Luma
90.8
84.3
80.2
95.7
94.2
79.1
98.4
98.6
41.4
89.4
47.7
72.5
99.3
88.0
92.0
83.44
Mira
20.6
33.6
21.4
33.3
24.2
12.4
32.6
42.0
98.7
25.3
24.4
16.6
29.5
79.7
36,93
Pika
77.2
64.3
59.1
81.0
87.1
68.2
91.1
82.6
21.9
97.2
43.9
65.6
80.3
83.9
72.1
71.69
Opensora VI.I
58.1
55.2
57.4
79.1
49.3
70.3
73.7
50.4
90.4
48.2
55.0
65.2
64.1
63.62
Apple-STIV
87.3
74.8
77.5
74.4
88.7
78.6
73.3
74.9
29.7
78.0
36.4
96.3
67.6
91.1
86.2
74.31
CiusVid
28.7
21.8
19.6
39.6
34,5
38.3
50.7
44.8
7.3
16.7
99.6
18.4
28.8
31.47
76.8
67.8
54.2
79.7
74.3
60.4
81.8
77.6
64.1
78.8
28.9
70.4
77.0
99.2
94.6
72.39
AnimateDift-V2
68.4
60.5
49.4
67.7
51.1
83.7
73.9
58.1
58.9
76.2
66.16
Table 10: Cross-Validation Validation Results. Each cell in the table represents the average recall (%) of four detection models (NPR (Tan et al.) (024)), TALL (Xu et al.) (023). X-CLIP-B/33 BB32 (Ni et al.) (002)), F3Net (Qian et al. 2020)). The model is trained generated videos of each subset and 5k real videos from MSR- VTT dataset.
are then tested on all other generative subsets. The average cross- cross- validation accuracy across the four detectors is reported in Table I0] Each element in the table represents the mean detection accuracy across the four models. Diagonal entries correspond in-subset evaluations, where the detector is tested the
generative model used for training. As shown in Figure we interpret the matrix M as a distance metric between generative models and apply Multidimensional Scaling (MDS) to project their relationships into 2D space. This visualization reveals clusters of architecturally sim- ilar models, such as AnimateDiff2(XX et al., 2024) and VideoCrafter autoregressive-baite based models, such (Yin et (025). appear distant from the rest. This map- ping also informs diverse training set selection of generative models, we could combine the cross validation accuracy and similarity construct high-quality and diverse dataset for data-efficient training.
et al.) 2024a), while
Better Generation, Better Detection. our cross-validation experiment, we observed that de- tection models trained higher-quality generated videos exhibit stronger detection performance.
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To validate this observation, we retrieved the overall VBench scores (Huang al. 2023 for each generative model and conducted a correlation analysis between these scores and the average detec- tion accuracies reported in Table 10 The results are visualized in Fig.1 of our main paper. Since the cross-validation data is directly sampled from VBench's evaluation set, the VBench scores pro- vide an accurate proxy for the generation quality of each subset. Across 14 models (excluding CausVid, which features a fundamentally different model structure and training paradigm), we com- pute Pearson correlation coefficient of p 0.86 between average detection accuracy and VBench scores, indicating strong positive correlation. Furthermore, when restricting the analysis to the six DiT-based models, the correlation increases to p 0.92. These results strongly support our hypoth- esis: among models with similar architectures, higher-quality generation leads to better supervision signals, enabling detection models to learn more effective forgery-discrmmnnat features.
C IMPLEMENTATION DETAILS
This section outlines the configurations and method, as well as the baseline models.
used for training our proposed
Our Method. For our detector and Moon-Vil all experiments are conducted using PyTorch with Automatic Mixed Precision (AMP) in bíloat16 to enable Flash Attention optimization and accel- erate training. The visual backbone is initialized with Vision Transformer (ViT) weights from the officially released Qwen2.5-VL. model. We explore multiple fine-tuning strategies with distinct hy- perparameter settings: (1) Full fine-tuning: We set the batch size to 4 and train for 5 epochs with a learning rate of le-5. (2) Linear Probing (LP) and Parameter-Efficient Fine-Tunine (PEFT): These approaches use larger batch size of 32 and a learning rate of le-4. Training continues for up to 30 epochs, with early stopping based on validation loss (patience 5 epochs) to prevent overfitting.
Other Baseline Methods. To ensure fair comparison, all baseline models are trained under a uni- fied experimental setup. We used consistent batch size of 32 and trained for maximum of 30 epochs, also employing an early stopping strategy with 5 epochs patience. The learning rate was adjusted based on the model architecture: for baselines utilizing CLIP VIT backbone, such as X- CLIP and CLIP-based detectors, we set the learning rate to 1e-6; for all other models, a learning rate of le-5 was used.
Data Pre- Pre-processing for Baseline Methods. A consistent data pre-processing pipeline is applied across all models during both training and testing. During training, each video is first sampled at rate of 2 frames per second, from which 8 consecutive frames are extracted. If a video contains fewer than 8 frames, it is padded with blank frames to meet the required sequence length. Each frame is resized such that the shorter side is 224 pixels, followed by random crop to a final resolution of 224x224. To enhance model robustness, we apply two forms of data augmentation: random horizontal flipping and random Gaussian noise. During testing, frames are sampled in the same manner as during training. After resizing the shorter side of each frame to 224 pixels, center crop to 224x224 is applied instead of random crop to ensure deterministic evaluation.
D ADDITIONAL RESULTS AND ABLATIONS
Full Results on Genvideo-Val Val. As shown in Table 11 our proposed method achieves state-of of-te- art performance across several key metrics. Notably, attains an F1 score of 90.64 and an average precision (AP) of 96.13, surpassing all other leading methods-iicluddngg DeMamba-CI.it which was trained on the VenVideo dataset comprising 2.2 million samples. contrast, our model was trained on only 140K samples, over ten times fewer, underscoring both the high quality of our train- ing data and the efficiency of our method in learning robust forgery-diserminnn resolution. In addition, our model achieves balanced accuracy (bACC) of 95.38, significantly outperforming all competing methods. This result demonstrates not only high overall detection per- formance but also the model's well-rounded and consistent capabilities across diverse forgery cases.
features at native
Efficiency Comparison. As detailed in Table 12 we conduct a comprehensive efficiency analy- sis comparing our proposed Qwen2.5-VL. ViT (Qwen2.5-ViT) with several strong baseline models.
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Model
Training Data
Metric
Sora
Morph Studio
Gen2
HoeShox
Lavic
Show-1
Valley
Cralter
Scope
Scrape
Arg.
UNITE
FaceFoennicss+. SAIL-VOS-3D
Recall
92.11
100.0
94.62
96.93
98.12
99,86
98.69
100.0
96.29
89.89
89.60
100.0
Recall
95.71
100.0
98.70
69.14
92.43
93.29
100.0
100.0
83.57
82,94
91.58
DeMamha- CLIP
GesViden
F1
64.63
96.15
97.59
78.03
94.
92.76
95,72
98.04
87.23
87.82
89.19
AP
85.50
100.0
99.59
76.15
96.78
96.99
99.97
100.0
89.50
89.72
93.45
RINE DeMamba
Plow
BACC
$3,80
92.20
62.00
79.50
72.60
92.40
87.50
63.60
78.10"
Corvi et al.
PyramidFlow
BACC
97.00
98.80
81.40
95.50
92,
98.40
98. 30
97.10
94,30"
Recall
82.14
97.14
99.49
89.00
96.79
92.29
99.05
99.07
83.00
71.60
91.16
Ours
15model-140k
AP
82.49
99.36
99.95
96.55
99.78
97.88
99.87
99.80
94.50
90.98
96.13
bACC
90.57
98.38
99.55
91.31
99.20
95.95
99.33
99.31
91.31
85.61
95,38
Table 11: Benchmarking Evaluation in terms of Recall, F1 score (F1), average precision (AP), and balance accuracy (bACC) on Genvideo-Val The results of RINE and DeMamba are reported in Corvi et al. 2025).
Model
Resolution
#Params
FLOPS
Peak GPU Mem
Training Time Epoch
21.5GB (bs=4)
CLIP-L
[224, 224]
303.2M
622.6G
9.5 A100 hours
129,3GB (bs=32)
21.5GB (bs=4)
X-CLIP-L
[224, 224]
429.2M
650.6G
10.5 A100 hours
129.3GB (bs=32)
17.3G (bs=4)
Effort
[224, 224]
0.2M/504.6M
623.4G
A hours
75.1GB(bs=333
Qwen2.5-Vi ViT
[224, 224]
668. 7M
656G
(6.0CI((hs4))
2.3 10000 hours
Qwen2.5. W-TT
dynamic [224p, 448pl
668. 668.7M 7M
37 (7s99)()s==)
7 A100 hours
Qwen2.5-ViT-LoRA ViT-LORA
dynamic [224p, 448p]
2.6M/671.31M
27.4GB(bs=4)
5.5 A hours
Table 12: Efficiency comparison results on model parameters, FLOPS, GPU memory utilization and time consumed during training.
For standard input resolution of [224, 224]. Qwen2.5-ViT exhibits remarkable training efficiency. Despite having more parameters (668.7M) M) than CLIP-L (303.2M), it achieves 4.1 x reduction in training time (2.3 vs. 9.5 A100 hours) and 25% decrease in peak GPU memory usage (16.0GB vs. 21.5GB batch size of 4). These gains are primarily attributed to efficiency-orientee de- sign choices such as bfloat16 training and Flash Attention, which allow Qwen2.5-ViT to utilize computational resources more effectively. When adopting dynamic resolution strategy, the train- ing overhead naturally increases, yet Qwen2.5- ViT remains faster and more memory-efficient than the baselines. Moreover, our parfmeter-efficient fine-tuning variant, Qwwnn2.5VVT-LLRR. requires updating only 2.6M parameters. This substantially reduces resource demands compared to full dy- namic fine-tuning, lowering GPU memory from 37.9GB 27.4GB and cutting training time from to 5.5 hours. Overall, these results highlight that the superior efficiency of Qwen2.5- 5-T stems from architectural optimizations, making the additional cost of higher dynamic resolutions acceptable in practice.
E VISUALIZATION AND DISCUSSION
Saliency Analysis. We examine the model's attention responses to better understand its discrimi- native behavior, as illustrated in Figure 6 The results confirm that our native-resolution framework effectively captures two key types of features crucial for AIGC detection. (1) Low-level Artifacts:
In billboard scenes, the model focuses on fine details such as distorted text rendering and unnatural edge transitions that are often lost during resolution downsampling. These high- high-frequency artifacts are indicative of generation errors and are critical for reliable detection. (2) High-level Semanties:
In the fruit-cutting examples, the model attends to global inconsistencies, including object defor- mations and unrealistic lighting, suggesting it captures holistic content-lewe anomalies. This dual focus demonstrates that our approach leverages both spatial fidelity and semantic context, validating the design choice of preserving native resolution.
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Figure 5: Video Visualization from Magic Video Benchmark. From left right, each column denotes videos from real sources, seaweed, seedance, and wan2.1.
PRESAEETE 2E0
PHEAAOR y0U
PHEMEER
PHENEEF COCA
0-20
Figure 6: Saliency Analysis. Saliency maps of our model on Al-generated video samples.
Figures 5 and to 9 present a selection of video samples from our dataset, with Figures 3-5 offering detailed visualizations along with their corresponding generative prompts. As illustrated in these figures, the videos in Magic Videos benchmark exhibit high visual quality, characterized by aesthetic appeal, rich motion, and diverse themes and visual effects.
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By using carefully curated prompts control the generative themes, able evaluate model's 's detection performance without introducing content bias. This methodological design pro- motes fairer and more reliable assessment, encouraging the detector to learn generalizable forgery artifacts rather than memorizing specific object- or scene-level patterns.
Acknowledgment of LLM Usage. This manuseript has benefited from the assistance of large language model, which was employed solely for grammar checking and language polishing. All scientific ideas, experimental designs, analyses, and conclusions are made by the authors.
real video
prompt: The video showeases the Alhambra in Granada, Spain, transitioning from warm golden sunset tones to deep violet hues as night falls. The palatial structures, set against the Sierra Nevada mountains and lined with cypress trees, shift from sunlit brilliance dramatic nighttime illumination. A subtle zoom enhances the view, while the changing light casts a striking contrast between the fortress's golden glow and the darkening sky, creating captivating visual transformation.
generated video
Figure 7: Video Visualization from Magic Video Benchmark.
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real video
prompt: A woman and man engage in friendly outdoor conversation amid wooden structures and greenery. The woman, wearing purple headband and green tank top, sips her drink, signaling relaxation. Her expressions shift from savoring engaging warmly, smiling and making eye contact. The man listens attentively, maintaining steady demeanor: Both hold beverages, emphasizing the leisurely tone. Their uninterrupted dialogue features moments of humor and enjoyment in serene setting.
generated video
Figure 8: Video Visualization from Magic Video Benchmark.
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Published as a conference paper at ICLR 2026
real video
n $
9999 M
$
44.0 Milli
prompt: The video showeases billboards for Powerball (S470M) and Mega Millions (S999M) under a sunny sky, with a '3 News Now' banner highlighting a '$1 BILLION
MEGA MILLIONS JACKPOT: Vibrant designs and mentions of 'NEBRASKA POWERBALL POWERPLAY' add local context. A brief error misstates the Mega Millions jackpot as S9M before correcting it. The video ends with a wide shot of the billboards against a residential backdrop, emphasizing their public appeal.
generated video
Aacabokk
$1,999
$1
MILLIIONS
MILLIOOSS
ORIEBON NOM
$11 PIEE NOW
TOWNECAL
FONROALL
Powerball
POWERSAIL $470M
PowerbalL
POWERBAIL $470M
Powerball
MANGEPAI $470M
Powerball
PPASTPAA
PA
PA
$470M
Figure Video Visualization from Magic Video Benchmark.
27


Reviews:
[{"review_id": "pRrqlpnfsX", "reviewer": "Reviewer_A394", "summary": "This paper addresses the pressing need for effective detection methods for AI-generated videos, which is a highly valuable area of research. The authors provide an in-depth exploration of the current state of research, highlighting its shortcomings, including the development of video generation models, detection of generated images, and existing methods for detecting generated videos. Two major challenges are identified in the detection of AI-generated videos: 1) The fixed-resolution preprocessing through operations like cropping and downsampling leads to information loss and coarse-grained detection; 2) Current detection methods are typically trained on outdated synthetic data sources, which are insufficient for handling videos produced by the latest high-quality generative frameworks.\nIn response to these issues, the authors construct a high-quality and diverse dataset sourced from state-of-the-art generative models and propose a novel detection framework built upon Qwen2.5-VL, which processes videos in their native spatial resolution and temporal length. This approach preserves crucial forgery artifacts often lost in conventional preprocessing steps, such as resizing or cropping.\nThrough extensive experiments, the authors demonstrate the effectiveness of their method and reasonably discuss the remaining challenges. Their work significantly advances the field, providing a robust foundation for future AI-generated video detection efforts.", "questions": "see the weakness", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.This paper addresses the significant limitations of existing methods for detecting AI-generated videos, highlighting the urgent need for effective detection strategies in light of the rapid development of video generation technologies.\n2. The authors construct a novel, large-scale dataset by utilizing cutting-edge video generation tools, which ensures that the dataset is diverse and high-quality, effectively supporting the proposed detection framework.", "weaknesses": "1.The section on \"3D Video Patchifying at Native Scale\" lacks sufficient novelty. Although the paper claims that the model is trained at native resolution, this approach was already introduced in Qwen2.5-VL [1]. The focus of the method seems to be more on engineering optimization rather than presenting a fundamentally new contribution to the field. Additional exploration of novel techniques or improvements beyond existing methods would strengthen this section.\n2.The paper lacks a more detailed analysis of the proposed method, particularly regarding how the model differentiates between real and generated content. While the authors claim the use of native resolution processing, there is little discussion on which specific parts of the video (e.g., temporal inconsistencies, high-frequency artifacts, or motion patterns) the model focuses on to distinguish between real and synthetic videos. A deeper exploration of the key features the model uses to make this distinction would help clarify the strengths of the approach and provide insights into its decision-making process.\n3.While the use of dynamic resolution processing significantly enhances performance, it introduces additional computational overhead. This increased complexity may limit the model’s feasibility for real-world deployment, particularly on resource-constrained devices or in scenarios requiring real-time detection.\n4.The proposed dataset structure is highly unreasonable; each class in the test set has very few samples, making the statistical results highly likely to be biased.\n5. The experiments were insufficient and were not tested on the latest dataset GenVidBench.\n[1]Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gqkUK22Pxe", "reviewer": "Reviewer_X5mz", "summary": "This paper investigates the limitations of AI-generated video detection, highlighting that fixed-resolution preprocessing removes high-frequency forgery artifacts and that outdated datasets struggle to represent modern models. The authors build a dataset of about 140K videos from 18 generative models and real sources such as Kinetics and MSVD, and design a realism-oriented test set called Magic Videos. They propose a detection framework based on Qwen2.5-VL ViT, which processes videos at their native spatial and temporal scales using 3D patch tokenization to preserve forgery details. Experiments are conducted on GenVideo, DVF, and Magic Videos.", "questions": "Regarding computational efficiency, the paper mentions optimization techniques such as Flash Attention, but lacks specific key metrics such as inference speed and memory usage. Could you provide detailed efficiency data under typical hardware configurations to assess the feasibility of this method in real-world scenarios?\n\nFor Magic Videos, how is the \"indistinguishable to humans\" claim verified?\n\nExperimental results show significant performance differences across different generators (from 72.26% to 85.12% in Table 2). Have you analyzed the specific reasons for these differences? Are there any specific types of generated content or technical approaches that are blind spots for the current method?\n\nCould you provide a more in-depth interpretability analysis, such as attention visualization or feature analysis, to demonstrate that the model truly learns meaningful forgery traces, rather than relying on other superficial features in the data?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "Builds a large-scale dataset with over 140,000 videos from 18 generative models, covering mainstream AI video generation technologies and offering a research resource.\n\nConducts experiments on multiple benchmarks, including the self-built Magic Videos test set and public datasets, ensuring comparability and reliability.\n\nEmploys a dynamic resolution processing framework that preserves detection performance and demonstrates generalization ability across datasets.\n\nDesigns ablation experiments that are systematic and support the main conclusions.\n\nIntegrates Flash Attention and LoRA optimizations, reflecting concern for efficiency and practical deployment.", "weaknesses": "Lacks analysis of the model’s decision-making mechanism, failing to verify whether the model truly learns forgery-related cues rather than biased features in the data.\n\nProvides insufficient discussion of computational efficiency, with no concrete results on inference speed or resource requirements for real-world deployment.\n\nThe training data source is limited, relying mainly on the VBench dataset, which may introduce hidden biases.\n\nThe failure case analysis is inadequate, lacking exploration of the scenarios and causes where the model fails.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TIVbjYsABV", "reviewer": "Reviewer_KYH3", "summary": "The paper proposes an AI-generated video detector that (1) builds a  dataset and (2) trains a detector on native spatial resolution and temporal duration using Qwen2.5-VL ViT with 3D patchification, so that high-frequency, position-dependent artifacts aren’t destroyed. On three families of benchmarks (GenVideo, DVF, and their Magic Videos), it reports consistently higher performance than prior image-detectors, deepfake-detectors, and video backbones. The core narrative is: current detectors are undertrained on modern, high-quality video generators, and downsampling design.", "questions": "1. How sensitive is the detector to platform compression? Your main argument is “don’t downsample to 224.” But if a platform already did that, can your model still outperform the older 224-trained models? A controlled experiment with platform-style compression would be convincing.\n2. How do you prevent source leakage? Since real videos come from specific real video datasets and generated ones are from VBench/MovieGen/etc., detectors might be learning source signatures. Do you have a cross-source test where the real videos share encoding with the synthetic ones?\n3. Happy to see more discussion with latest related work in the related area. This would help make the contribution clear.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Timely problem & data refresh. Most detectors are indeed lagging behind 2024–2025 video generators; this paper explicitly targets that gap and gathers content from new models, including commercial/API ones. That’s rare and useful.\n2. Clear empirical story about resolution. The experiment results supports the authors' claim well and the motivation is clear.\n3. The paper is well-written and easy to follow.", "weaknesses": "1. The paper addresses two real but relatively well-recognized issues — resolution-destructive preprocessing and stale generator distributions. The proposed solution mainly combines (i) a recent, high-capacity video/VL backbone and (ii) a refreshed multi-generator dataset. The detector architecture itself is largely standard, without forgery-specific inductive biases. Data curation process is also well defined in previous fake video detection work. Thus the contribution is more of an engineering consolidation.\n2. Dataset release / licensing / reproducibility is unclear. Several of the listed video sources (Kuaishou, Luma, MovieGen) are API/commercial. The paper doesn’t yet make it clear what exactly will be released, how prompts will be shared, and how others can reproduce “Magic Videos”. For ICLR this is important.\n3. No fairness breakdown. Real videos come from different sources than synthetic ones; there can be source, watermark, or codec biases. The paper doesn’t fully rule that out. It's important to demonstrate the performance on unseen real videos from very different source such as KITTI, etc. to demonstrate the generalizability of the proposed model.\n4. Limited analysis on real-world perturbations. The core claim is about preprocessing destroying artifacts, but actual attackers / platforms will introduce their own compress-and-resize chains. It would be good to see: scale jitter & heavy H.264/HEVC compression and see whether “native-scale” still wins.\n5. Incomplete discussion of most recent related work such as [1, 2]. \n\nReference\n1. Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features. 2024\n2. How Far are AI-generated Videos from Simulating the 3D Visual World: A Learned 3D Evaluation Approach. 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ff31uFjGXf", "reviewer": "Reviewer_9HXd", "summary": "This paper explores AI-generated video detection. It curates a dataset consisting of videos created from 15 or 18 generative models, as well as a novel framework that processes videos at their native spatial resolution and temporal duration, avoiding destructive preprocessing like resizing and cropping that mess up subtle forgery artifacts. The framework is built on the Qwen2.5-VL Vision Transformer, which uses 3D patchification directly on the raw video tensor, preserving the high-frequency details essential for detection. The experiment result shows improvement compared to baseline methods.", "questions": "1. Can baseline detection methods be retrofitted to use the high-resolution inputs for training and inference? \n2. Following the above question, if given high-resolution input in both training and inference, how does the baseline method compare with the proposed method?\n\n3. Can the artifacts be identified or visualized?\n4. Following the above question, can the artifacts be removed or interfered with so that the detection method fails?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. AI-generated video detection is an important topic\n2. Providing a curated dataset of Gen-AI videos is a good contribution\n3. The proposed detection framework shows improvement compared to baseline methods.", "weaknesses": "1. The proposed detection methodology is simply a combination of existing approaches and does not present a novel contribution\n\nDetail: The core detection framework is a direct application of the existing Qwen2.5-VL Vision Transformer using its native 3D patchification strategy, which the authors note is adopted from prior work (Bai et al., 2025). While applying this to the forgery detection task is a valid contribution, the paper presents it as a 'novel detection framework'  when the architectural novelty itself is minimal. The primary novelty seems to be the dataset and the hypothesis that native-scale processing is superior, rather than a new detection architecture.\n\n2. The presence of artifacts is vaguely assumed but not explicitly defined or proven to exist.\n\nDetail: The authors do not define, visualize, or analyze what these artifacts are. A significant weakness is the lack of any model explainability (e.g., activation maps or gradient-based analysis) to prove that the native-scale model is actually focusing on these \"pixel-level artifacts\", while the 224p-resized model is not. The entire premise, while intuitive, is treated as an assumption rather than a proven scientific finding.\n\n3. The use of downgraded input by cropping or resizing is argued to be the main fault of previous works. However, it is not explained or experimented with in connection with the actual baseline used.\n\nDetail: Since the proposed method also performs poorly when given low resolution input, it is not surprising that baseline methods perform poorly when given low resolution input. This makes it impossible to know if the proposed method is superior due to its architecture or simply because it's the only one allowed to see the high-resolution data. A fair comparison would require adapting baselines to also accept high-resolution inputs or, at a minimum, comparing all models at the same fixed resolutions (e.g., 224p, 480p, 720p).\n\nOther issues:\n- The proposed detection method is shown to have a much higher cost in training with memory and GPU hours.\n- Discrepancy in the number of generative models used (abstract: 16; Introduction: 15 and 18; appendix: 15)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)