Title: Seeing What Matters: Generalizable AI-generated Video Detection with Forensic-Oriented Augmentation
Venue: NeurIPS 2025
Accepted: yes
Keywords: generative AI

media forensics

synthetic video detection
Abstract: Synthetic video generation is progressing very rapidly. The latest models can produce very realistic high-resolution videos that are virtually indistinguishable from real ones. Although several video forensic detectors have been recently proposed, they often exhibit poor generalization, which limits their applicability in a real-world scenario. Our key insight to overcome this issue is to guide the detector towards _seeing_ _what_ _really_ _matters_. In fact, a well-designed forensic classifier should focus on identifying intrinsic low-level artifacts introduced by a generative architecture rather than relying on high-level semantic flaws that characterize a specific model. In this work, first, we study different generative architectures, searching and identifying discriminative features that are unbiased, robust to impairments, and shared across models. Then, we introduce a novel forensic-oriented data augmentation strategy based on the wavelet decomposition and replace specific frequency-related bands to drive the model to exploit more relevant forensic cues. Our novel training paradigm improves the generalizability of AI-generated video detectors, without the need for complex algorithms and large datasets that include multiple synthetic generators. To evaluate our approach, we train the detector using data from a single generative model and test it against videos produced by a wide range of other models. Despite its simplicity, our method achieves a significant accuracy improvement over state-of-the-art detectors and obtains excellent results even on very recent generative models, such as NOVA and FLUX.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe submission proposes a combination of two data augmentation strategies relying on a very recent video encoder (Pyramid Flow, ICLR 2025) for the detection of ai-generated videos.\n\nThe first data augmentation strategy (Section 3.2) relies on encoding real videos in an autoencoder fashion.\nThe second data augmentation strategy (Section 3.2, fig 4) runs a Haar transform on an AI-generated video, embeds for the low band the frequencies taken from real videos and inverts it to create an altered AI-generated video. This aims to force the method to look at mid to high frequencies.\n\nThey observe that several SOTA methods which perform well on videos from generators of 2022-2023 perform worse on videos from generators of 2024-now.\n\nThey show good performance for video detection with their method.  They show that retraining the other SOTA detectors does not close the gap to their proposed method fully.\n\nThey show that one image-based method improves substantially and gets close to the proposed method when retrained using the encoder from Pyramid Flow.\n\nMore Detailed technical descriptions for the implementation can be found in the supplement section D.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- easy to read\n- simple idea\n- training details in the supplement are available\n\n- ablation study that attempts to use the proposed encoder with other near-SOTA methods\n- ablation study on data augmentation\n- ablation study on backbones\n\nWeaknesses:\n- somewhat limited novelty\n\nQUESTIONS:\n- Can you place a link to section D of the appendix in the main paper in section 4 ?\n \n- A graphic with the full prediction model (incl link to the backbones discussed) in the supplement would be helpful .  That would help to consolidate the information which is a bit scattered in  the main paper.\n\nLIMITATIONS:\nThey discussed the limitations meaningfully.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 2, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the critical challenge of generalizing AI-generated video detection by proposing a novel forensic-oriented data augmentation strategy. Recognizing that existing detectors often struggle due to reliance on high-level, generator-specific semantic artifacts, the authors shift the focus towards intrinsic low-level, frequency-domain traces that are consistent across various generative architectures. Specifically, the paper introduces a wavelet-based augmentation strategy that selectively emphasizes mid-high diagonal frequency components robust to video compression artifacts. The proposed method is evaluated using videos from multiple generative models, achieving substantial improvements over current state-of-the-art methods, especially in cross-generator generalization scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nQuality:\n1.The methodology is rigorous, clearly motivated by a thorough analysis of the frequency-domain forensic artifacts inherent in synthetic video generation.\n2.Experiments are comprehensive and well-structured, using diverse datasets including recent generators and established benchmarks.\n3.Metrics used (AUC, balanced accuracy, Pd@5%, NLL, ECE) are relevant and provide a nuanced evaluation.\nClarity:\n1.The paper is very clearly written, with logical progression from motivation through method, results, and discussion.\n2.Figures effectively illustrate the key insights about forensic artifacts and the proposed augmentation approach.\nSignificance:\n1.The approach directly addresses a significant limitation in current forensic video detection research, offering practical strategies for real-world applicability.\n2.Results demonstrate meaningful improvements in generalization, a critical aspect given the rapidly evolving nature of generative technologies.\nOriginality:\n1.The wavelet-based augmentation strategy is novel within the context of forensic-oriented video detection.\n2.The explicit focus on intrinsic, compression-resistant frequency bands is a valuable innovation in the forensic detection literature.\n\n\nWeaknesses:\n1.The method primarily focuses on spatial frequency components, explicitly noting a limitation in addressing temporal artifacts, which could be important in video detection tasks.\n2.While the approach improves generalization substantially, the robustness of this augmentation strategy to drastically different future generative paradigms remains somewhat uncertain.\n\nQUESTIONS:\n1.Temporal Artifacts: Given the promising results, how could the proposed augmentation strategy be extended or adapted to explicitly include temporal forensic artifacts, which could further enhance detection performance?\n2.Future Generators: How robust do you expect your method to be against significantly different generative paradigms that may emerge in the future? What adaptations, if any, would be necessary to maintain high generalization?\n3.Computational Efficiency: While wavelet decomposition is mentioned as computationally efficient, could you provide specific benchmarking or computational complexity analyses comparing this augmentation strategy to simpler augmentations such as MixUp or CutMix?\n4.Dataset Diversity: Do you anticipate similar performance benefits when applying your augmentation strategy to significantly smaller or differently structured training datasets?\n\nLIMITATIONS:\nYes, the authors clearly and effectively addressed limitations and potential negative societal impacts. They acknowledge explicitly the method's limitation in focusing primarily on spatial artifacts and highlight the potential need for adaptation to new generative paradigms.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nRecently, with the rapid development of video generative models, forensic detectors have also emerged; however, many of them lack generalizability. To address this issue, the authors propose a method that first identifies discriminative features that are less biased toward forensic-irrelevant patterns and more robust for detecting generated videos. Based on frequency domain analysis and previous literature, the authors identify that mid-high frequency components are less susceptible to compression artifacts and are thus suitable for forensic detection. To utilize these cues, two novel augmentation strategies are introduced during training: (1) Injection of forensic cues, by reconstructing real videos using the same autoencoder that generated the fake videos, aligning both real and fake samples in the representation space, and (2) Wavelet-based augmentation, by mixing real and fake videos using wavelet decomposition to enhance the mid-high frequency learning and push the detector to focus on those features. In the experimental evaluation, the model was trained using videos generated by the Pyramid Flow model, and testing was performed on both the GenVideo dataset (publicly available) and a newly constructed dataset comprising 2,400 videos from recent generative models. Accuracy was used as the evaluation metric, and the proposed method outperformed recent forensic detection methods.\n\nSTRENGTHS AND WEAKNESSES:\n1. Quality: \nThe submission is technically sound, presenting a relevant formulation and effective use of forensic feature extraction from videos. The claims are well-supported by experimental results. The methods employed are appropriate, and the work is presented as a complete and cohesive study. The authors are transparent about both the strengths and limitations of their approach.\n\n2. Clarity:\nThe submission is clearly written and easy to follow.\n\n3. Significance:\nThe results are impactful for the community. While the code and new dataset have not been released, if shared, this work has strong potential to be used by other researchers for further development and benchmarking.\n\n4. Originality:\nThe work integrates several ideas from previous methods, with appropriate citations. Although the application of these ideas to video forensics is a novel context, the core techniques themselves are adapted rather than newly proposed. Therefore, the method is not entirely novel in its formulation.\n\nQUESTIONS:\na. In lines 186–193, the authors state that replacing the low-frequency bands with those from the real counterpart forces the detector to focus on mid-high frequency features. However, how can we confidently claim that the detector will focus on mid-high frequencies? It is possible that the detector still learns from the low-frequency content provided by the real counterpart, potentially leading to misleading or incorrect detection. Do the authors have empirical evidence or observations supporting this assumption?\nb. The proposed method appears to heavily rely on the reconstruction quality of the Pyramid Flow model. While the paper mentions that this model generally reconstructs videos without visible artifacts, what happens if it fails in certain cases? Would such failure compromise the robustness or generalizability of the detector trained using this reconstruction-based augmentation?\nc. The paper makes valuable contributions, including a new dataset and a promising method. However, neither the code nor the dataset has been made available. The authors are strongly encouraged to release both the code and dataset to enable transparent evaluation, reproducibility, and faster progress in the rapidly evolving field of forensic video detection.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel training paradigm for synthetic video detection using forensic-oriented data augmentation based on wavelet decomposition. The authors claim to identify discriminative features in mid-high diagonal frequencies that remain robust across different video generators against compression, and introduce wavelet-band replacement augmentation to redirect model focus from semantic artifacts to forensic cues. The method achieves 10+% bAcc improvement over existing detectors while only being trained on a single generator. The core contribution is to augment the data by preserving forensic artifacts while reducing semantic bias, enabling better cross-generator generalization.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The foundation and motivation of the paper is very clear, connected to implementation and design choices, and related with the literature. The authors' focus on mid-high diagonal frequency components aligns with established research on GAN-specific frequency signatures. The choice of wavelet decomposition is well-justified, as wavelets preserve spatial information, while enabling multi-scale frequency analysis, while also being computationally more efficient. \n\n- Forcing models to focus on intrinsic forensic cues rather than semantic differences is a strong motivation and is implemented clearly. It would have been even better if the paper documented (or referred) to the current landscape of detectors battling with this problem. \n\n- Training only on one generator and generalizing to 15 generators is an excellent evaluation for generalization, especially for a production environment. The comparison tables, though, need another pass.\n\n- Very interesting observations on the relation between compression and generative fingerprints, which leads to mid frequency components. A compression ablation study would be the cherry on the top.\n\nWeaknesses:\n\n- There needs to be a clear distinction between \"generation-specific artifacts\" vs. \"artifacts introduced by specific generators\", else the paper is trying to solve what is claimed to be solved by other methods. \n\n- There is very little information on the denoiser, which can be very influential on the results. An ablation study with/without would enhance the design choice.\n\n- To further emphasize the claims of the paper, I would be very interested to see a result where the same method is trained/tested on the training/testing set of all 15 generators. That would be a showstopper if training the same model on 15 generators performs worse than training on one augmented generator. If only Pyramid Flow training is causing such high performance, I would be excited to see the training on more generators. Then it opens the doors for yet another research question: is augmentation all we need (for df detection)? :)\n\n- While it is known that compression helps with deepfake detection evasion as an adversarial strategy, the paper needs stronger empirical validation of compression robustness claims with systematic evaluation across multiple compression standards and quality levels, possibly with different frequency decompositions for the wavelet.\n\n- I believe Tab. 3 is neither fair nor relevant for the claims of this paper. If the contribution of this paper is the new Wavelet-based augmentation method, then augmenting the training sets of these detectors and then observing their accuracy increase would be a fair comparison. If the contribution is the DINOv2 + augmented data, then it should have been trained/tested on the detectors' datasets. Tab. 4 comes closer to a fair ground, however the contribution is still unclear here: DINOv2 backbone itself is not making this difference, is it the augmented Pyramid Flow data + DINOv2? What happens if we augment the training data of all?\n\n- I would assume compared detectors to have some commonality with this detector, may it be generalization, or fingerprints, or frequency-based analysis. There are many other detectors that might have been compared which share similar motivations, like other frequency domain approaches (DCT-based methods, Fourier analysis). \n\n- While the paper identifies mid-high diagonal frequencies as discriminative, the analysis lacks depth compared to established frequency domain research. More in-depth analysis (or even classification) of spectral artifacts different generators or a quantitative frequency band analysis would ground the observations.\n\n- The wavelet-band replacement augmentation procedure needs clearer algorithmic description.\n\nQUESTIONS:\nMinor suggestions:\n- Missing detectors based on temporal forgery cues [1], motion decomposition [7], frequency analysis [5,6], augmentation [4], attention [8], and generalization [2,3]. Not all of them need to be compared against, but the related work section lacks comparison.\n- Specifically for source generator detection for deepfake videos (line 137), there are more relevant work [7,9].\n- A real challenge (and validation) would be evaluating the proposed method and others on Deepfake-eval-2024 dataset, which is claimed to be truly in the wild. \n- Size of training/validation sets are significantly low.\n- The order of the results is weird, talking about ablation before results is not conventional. \n- Mixup and Cutmix cites can be moved to line 263.\n\n\n[1] Guo, Z., Liu, Y., Zhang, J., Zheng, H., & Shan, S. (2025). Face Forgery Video Detection via Temporal Forgery Cue Unraveling. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 7396-7405).\n\n[2] Wang, Z., Bao, J., Zhou, W., Wang, W., & Li, H. (2023). Altfreezing for more general video face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4129-4138).\n\n[3] Yan, Z., Zhao, Y., Chen, S., Guo, M., Fu, X., Yao, T., ... & Yuan, L. (2025). Generalizing deepfake video detection with plug-and-play: Video-level blending and spatiotemporal adapter tuning. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 12615-12625).\n\n[4] Yan, Z., Luo, Y., Lyu, S., Liu, Q., & Wu, B. (2024). Transcending forgery specificity with latent space augmentation for generalizable deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8984-8994).\n\n[5] Kashiani, H., Talemi, N. A., & Afghah, F. (2025). FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 8775-8785).\n\n[6] 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 Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 5, pp. 5052-5060).\n\n[7] Demir, I., & Çiftçi, U. A. (2024). How do deepfakes move? motion magnification for deepfake source detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 4780-4790).\n\n[8] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., & Yu, N. (2021). Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2185-2194).\n\n[9] Çiftçi, U. A., Demir, I., & Yin, L. (2024). Deepfake source detection in a heart beat. The Visual Computer, 40(4), 2733-2750.\n\nLIMITATIONS:\nLimitations of the approach are well discussed. The paper makes a valuable contribution to synthetic video detection by proposing a practical augmentation strategy that utilizes generation artifacts, claimed to be generalizable across multiple datasets and generators. The paper addresses an important problem with a technically sound approach and demonstrates promising results. With proper validation of compression robustness claims, technical details and ablation studies on the frequency bands, better comparison organization, and inclusion of recent baselines, this work could significantly advance the field's understanding of forensic-oriented detection methods.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}