Title: AI-Generated Video Detection via Perceptual Straightening
Venue: NeurIPS 2025
Accepted: yes
Keywords: AI-Generated Video Detection

Perceptual straightening

Representation geometry

Deepfake Detection

Video Forensics

AI Safety
Abstract: The rapid advancement of generative AI enables highly realistic synthetic video, posing significant challenges for content authentication and raising urgent concerns about misuse. Existing detection methods often struggle with generalization and capturing subtle temporal inconsistencies. We propose $ReStraV$ ($Re$presentation $Stra$ightening for $V$ideo), a novel approach to distinguish natural from AI-generated videos. Inspired by the ``perceptual straightening'' hypothesis—which suggests real-world video trajectories become more straight in neural representation domain—we analyze deviations from this expected geometric property. Using a pre-trained self-supervised vision transformer (DINOv2), we quantify the temporal curvature and stepwise distance in the model's representation domain. We aggregate statistical and signals descriptors of these measures for each video and train a classifier. Our analysis shows that AI-generated videos exhibit significantly different curvature and distance patterns compared to real videos. A lightweight classifier achieves state-of-the-art detection performance (e.g., $97.17$ % accuracy and $98.63$ % AUROC on the VidProM benchmark, substantially outperforming existing image- and video-based methods. ReStraV  is computationally efficient, it is offering a low-cost and effective detection solution. This work provides new insights into using neural representation geometry for AI-generated video detection.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nGrounded on the perceptual straightening hypothesis, this paper presents an innovative approach to distinguish between natural videos and AI-generated videos by leveraging the distance and curvature between frame features. The simplicity and effectiveness of this method are commendable, and its relatively low computational cost is also a significant advantage. By solely utilizing statistical metrics in the perceptual straightening space to train classifiers, the authors have achieved remarkable results, which offers a fresh and valuable perspective on the detection of AIGC-detection.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n1.\tQuality: The paper presents a well-constructed hypothesis regarding the trajectory patterns in natural versus AI-generated videos. The authors provide substantial evidence to support their claims, demonstrating thorough research and analysis. The detection pipeline they developed, utilizing a standard ViT and lightweight classifiers, appears to be robust and effective, as evidenced by the significant results across multiple benchmarks. The quality of the research is further enhanced by the authors' meticulous attention to detail in highlighting the distinctions between the two types of videos.\n2.\tClarity: The use of factual evidence to illustrate the differences between natural and AI-generated videos is particularly effective in enhancing clarity.\n3.\tSignificance: The findings that natural videos tend to have smoother and more consistent trajectories compared to the irregular transitions in AI-generated videos offer valuable insights. The significant results across numerous benchmarks further highlight the practical importance of this research.\n4.\tOriginality: The paper introduces a novel perspective on detecting AI-generated videos by focusing on perceptual straightening and trajectory patterns. This approach is innovative and represents a departure from traditional methods.\nWeaknesses: \nIn line622, it mentions that Fig 10 shows the differences between RF and LR, but the caption of the figure states that it illustrates the differences between MLP and LR. Which one is correct?\n\nQUESTIONS:\n1. Compared to RF, what are the advantages of MLP? Is the computational time cost of RF measured on the CPU?\n2. Could the differences detected here be due to noise interference? If AI-generated videos are augmented with simulated noise or compression artifacts, would the algorithm still be able to distinguish them effectively?\n3. How many input frames are acceptable for this algorithm and the compared algorithms? Is it because we input more frames that we have a stronger ability to distinguish?\n\nLIMITATIONS:\nYes, it mentioned that developers of video models might optimize them in a way that deceives detection measures. However, it is necessary to propose advanced detectors. We should be more capable of distinguishing between real and fake videos than those who create AI-generated videos.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes ReStraV, a novel method for detecting AI-generated videos by analyzing temporal curvature and distance in neural representations. Inspired by the \"perceptual linearization\" hypothesis. ReStraV uses the pretrained DINOv2 visual transformer to quantify deviations in video trajectories. It shows that AI-generated videos have distinct curvature patterns compared to real videos. The method achieves state-of-the-art performance, with 97.17% accuracy and 98.63% AUROC on the VidProM benchmark, offering an efficient, low-cost solution for AI video detection.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Clarity of Writing: The paper is clearly written and accessible, making it easy to follow the motivation and methodology.\n2. Strong Empirical Results: The method shows significant improvements in detection performance, with accuracy increasing from 50–80% to 90–99% on benchmark datasets (e.g., Table 2).\n3. Motivated by Prior Perception Theory: The use of curvature and distance in representation space is well-motivated by perceptual neuroscience literature.\n\nWeaknesses\n1. Limited Evaluation Scope: The experiments only evaluate one commercial video generation model (Pika). The method should be tested on a broader range of state-of-the-art generators such as Sora, Gen-3, Luma, Kling, and Vidu to validate generalization.\n2. Limited Novelty: The core insight of the paper (e.g., the left and middle parts of Figure 1) appears closely derived from prior work [1, 2].\n3. Potential Vulnerability to Adversarial Adaptation: It remains unclear whether the proposed detection method can be circumvented if curvature and distance metrics are explicitly regularized during the training of generative models.\n\n[1] Olivier J Hénaff, Robbe LT Goris, and Eero P Simoncelli. Perceptual straightening of natural videos. Nature neuroscience, 22(6):984–991, 2019.\n[2] Olivier J Hénaff, Yoon Bai, Julie A Charlton, Ian Nauhaus, Eero P Simoncelli, and Robbe LT Goris. Primary visual cortex straightens natural video trajectories. Nature communications, 12 (1):5982, 2021.\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a perceptual straightening hypothesis-based approach to discriminate real videos from AI-generated ones. Specifically, statistics related to distance and curvature of video representations derived using an off-the-shelf SSL model are used as features to train a simple MLP. The performance is shown to be competitive on the chosen set of datasets against image-based, video-based, and MLLM-based baselines.\n\nSTRENGTHS AND WEAKNESSES:\n** Strengths**\n- The proposed perceptual straightening approach to discriminate read videos from AI-generated ones is lightweight and delivers good performance on the tested datasets.\n\n**Weaknesses**\n- The premise of perceptual straightening in SSL models is not justified. The original work [1] demonstrates straightening using bandpass filter plus nonlinearity models for the initial layers of the HVS. Further, [24] shows that SSL models can benefit from the straightening constraint. However, the proposed work assumes that the latent space of an off-the-shelf SSL model inherently straightens representations of natural videos. Further, the evidence of straightness using tSNE plots in Figure 4 is difficult to comprehend due to clutter. Furthermore, and crucially, Figure 8 (in A.2) does not provide conclusive evidence.\n\n- Importantly, a discussion on why the proposed method works is limited. Specifically, why do SOTA generators like Sora behave the way they do in the latent space of SSL models?\n\n- Also, the choice of the SSL model is rather adhoc. \n\n- The paper does not cite relavant works from the perceptual quality assessment literature. These works are relevant since they use the notion of \"straightness\" to discriminate pristine (or real) videos from distorted (or unnatural) ones. Given this, the novelty of the proposed paper is limited. Please see for example: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10667010, https://ieeexplore.ieee.org/abstract/document/9633248. \n\n- The results are compared against three different approaches - image-based, video-based, and MLLM-based. However, the comparison is missing relevant works such as TALL, NPR, STIL, DeMamba etc.\n\n- Please do a grammar check.\n\nQUESTIONS:\n- Have the authors considered using the bandpass filters and nonlinearities in [1] for generating video representations? This would not only justify the straightening hypothesis but would also provide an interesting comparison. Further, [1] and [24] show that off-the-shelf models do not innately straighten natural videos. Therefore, such an experiment would be illuminating.\n\n- Can you please suggest a more systematic approach to choosing an SSL model? The current methodology is completely empirical and adhoc.\n\n- Why have standard baselines not been considered in the performance comparison? Please explain.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces ReStraV, a novel method to distinguish natural videos from AI-generated ones. It utilizes the \"perceptual straightening\" hypothesis, observing that real video trajectories are straighter in latent space than synthetic ones. ReStraV computes temporal curvature and stepwise distance between frame embeddings of a pre-trained image encoder (DINOv2), which are used to train a lightweight classifier. The approach is computationally efficient, offering a robust and low-cost solution to detect synthetic content in videos.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- This is a very simple, light-weighted, effective method for detecting AI generated videos. Simplicity here is a pro and not meant to be a criticism.\n\nWeaknesses:\n- The paper can benefit greatly from a robustness analysis. I’m interested to know whether the classifier is robust to 1) choice of vision encoder 2) the time interval between frames on which distance and curvature are computed 3) duration of the video 4) types of artifacts in synthetic videos 5) the absence/presence of any one of the 21 features that are put into the classifier (which feature plays the most important role?)\n- Natural videos often have shot transitions. I would assume the straightening hypothesis only holds within one shot, or a short period of time. This constraints the scope of the method.\n\nSuggestions for improvement:\n- It would be nice to show a few examples of real videos and their “synthetic replicas”, to let readers have an idea of 1) the common types of artifacts in synthetic videos 2) how realistic the synthetic videos look like, so that we can have a sense of how difficult the detection problem is.\n\nQUESTIONS:\n- I’m curious to know how a video-level embedding with 3D convs or transformers interacts with the straightening observation. For example, whether the artifacts in synthetic videos are smoothed out? If so, would the straightening classifier still work?\n- Why include the CLS token? It seems to have a different nature than patch embeddings.\n- In section 6, how was the “seven distance values” chosen? Why 7?\n- It is known that a vanilla deep vision encoder does not straighten (meaning its latent space is not straighter than the pixel space), but models that are adversarially trained, or directly trained on the straightening loss, can produce much straighter trajectories. Would these models work better or worse for the classifier?\n\nLIMITATIONS:\nThis is a very simple approach, so the significance comes from the practical usage. However, I worry that pairwise distance and curvature is just a temporary “bias” of those video generative models rather than an intrinsic property. For example, they are often prompted to generate dynamic scenes (few prompts will ask the model to output a near stationary video), or they often fail at object persistence, which may have given rise to the larger frame-wise distance. If the prompt is carefully designed to generate slow or little motion, would the classifier still work?", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 4, "DATE": "2025-07-03"}