AI-Generated Video Detection via Perceptual Straightening
Authors: Christian Internò
Robert Geirhos
Markus Olhofer
Sunny Liu
Barbara Hammer
David Klindt
Venue: NeurIPS 2025 poster ()
Keywords: AI-Generated Video Detection
Perceptual straightening
Representation geometry
Deepfake Detection
Video Forensics
AI Safety
Primary area: neuroscience_and_cognitive_science
TL;DR: We propose ReStraV, a novel method to detect AI-generated videos by exploiting the geometric "straightness" of video trajectories in neural representation space.

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.

AI-Generated Video Detection via Perceptual Straightening
Christian Internò 1.3+ 1.3+ Robert Geirhos
Markus Olhofer?
Sunny Liu
Barbara Hammer!
David Klindt
Bielefeld University
2 Google DeepMind
Honda Research Institute EU *Cold Spring Harbor Laboratory
Abstract
The rapid advancement generative Al enables highly realistic synthetic videos, 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 ReStra V (RRepresentation Straightening for Video), a novel approach to distinguish natural from Al-generated videos. Inspired by the "perceptual straightening" hypothesis 2 which sug- gests real-world video trajectories become more straight in neural representation domain-we analyze deviations from this expected geometrie property. Using pre-trained self-supervised vision transformer (DINOv2), we quantify the tem- poral curvature and stepwise distance in the model's representation domain. We aggregate statistics of these measures for each video and train a classifier. Our analysis shows that Al-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., 17% accuracy 98.63% AUROC the VidProM benchmark (3), substantially outperforming existing image- and video-based methods. ReStraV is computationally efficient, offering a low-cost and effective detection solution. This work provides new insights into using neural representation geometry for Al-generated video detection.
Al-Generated
Trajectories in representation domain
Classifier: Al-generated natural
SSE (ng. DINOVZ)
2
Ziel
Classifier (egi MLPI
Natural
O(')
felm:)
Al-Generated vs. Natural
21
2/
23
Frames are processed by SSE and we collect the embeddings.
In representation SSE space, natural videos trace straighter paths than Al- generated
The trajectory geometry provides
discriminative signal.
The ReStraV method for AI-video detection. Inspired by "perceptual straightening." our approach leverages the geometric insight that natural videos form "straighter" feature trajectories (2;) than generated ones. The temporal curvature (Eq. serves as the discriminative signal for detection.
Figure 1:
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39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Early ap- proaches (e.g., generative adversarial networks, variational autoencoders) struggled with fidelity and temporal coherence 772. However, rapidly evolving large-scale foundation models have intro- duced sophisticated generative techniques [I These methods, often diffusion models [12] and transformer-based architectures [T3] [4] can produce near-photorealistic videos from text initial frames. As these systems improve, the ability to easily generate convincing synthetic videos raises pressing concerns about malicious manipulation and fabricated visual media [15]
Generative AT has significantly advanced in synthesizing realistic video content 4
Robust strategies to detect Al-generated content are therefore urgently needed
16 Detecting
AI videos is more challenging than Al generated image due temporal consistency requirements that necessitate thorough analysis across frames [7]. Traditional deepfake detectors, often tuned to specific artifacts (e.g., faap-ing irregularities), may not generalize to diverse generative methods [18. Moreover, large-scale pretrained foundation encoders may not explicitly learn features optimized for AI content defection. Watermarking is one option but relies on model operators' goodwill and can be circumvented [19H22]. Thus, detection methods are needed that capture Al generation anomalies, regardless of the underlying generative approach.
This work explores neural representational distance and curvature (formally defined in Eq. as discriminative signals for fake video detection. An overview of ReStraV is provided in Fig. According to the perceptual straightening hypothesis, natural inputs map to straight paths in neural representations while unnatural sequences form curved trajectories neuroscience, psychophysics, on CNNs and LLMs [ 23). It is motivated by the idea that predictive coding might favor straight temporal trajectories in latent space because they are more predictable.
2 This has been verified in
Taking inspiration, this work, we hypothesize a distinction between natural and Al generated videos in artificial neural networks (ANNs). While ANNs may not perfectly replicate biological straightening [ 2 [24], we expect their learned representations to show Al-generated videos as more curved in activation space than real videos. We surmise synthetic videos exhibit curvature patterns deviating from the lower curvature trajectories of real events, supported by differing ANN representational dynamics for natural versus artificial videos [24), as illustrated in Fig. A and Fig. 4
To test this hypothesis, we use the DINOv2 ViT-S/14 pretrained visual encoder 25 chosen for its sensitivity to generative artifacts (Fig. BB). For each video, we extract frame-level complete set of patch embeddings and Classify token (CLS) from DINOv2's final transformer block (block. 11). From this trajectory, we quantify local curvature (angle between successive displacement vectors, measuring path bending) and stepwise distance (change magnitude between consecutive frame representations), as in Eq. (I) We then derive descriptive statistics (mean, variance, min, max; examples in Fig. (5) from these per-video time series curvature and distance. These aggregated geometric features (Section 5) are used by lightweight classifier (Section 6 to distinguish real from Al content.
ReStraV V re-purposes DINOv2 as "feature space" for temporal anomalies. DINOv2's s extensive train- ing on natural data provides a latent space where real video trajectories should be characteristically smooth or "straight" (Fig. A Fig. 4. Deviations, like increased jitter or erratic curvature often in Al videos, become discernible geometric signals of synthetic origin. Importantly, ReStraV V is computa- tionally efficient, processing videos in approximately 48 ms end-to-end (including DINOv2 forward pass). ReStraV is thus a low-cost alternative to resource-intensing methods (details in Section 6). By exploiting ANN's activation dynamics, V offers a simple, interpretable Al-video detection approach (cxperimental validation in Section 7 Our contributions are as follows:
1. We propose a novel, simple, cost-efficient and fast representational geometry strategy for Al Al-generated video detection, leveraging neural activation distance and curvature as reliable indicators of generated videos.
2. We show the approach yields a reliable "fake video" signal across vision encoders, even those not trained on video data; DINOv2's 25 self-supervised representations excel without task-specific tuning.
3. We demonstrate through extensive experiments on diverse benchmarks (VidProM [3] Gen- VidBench [17], and Physics-IQ (26)) and models, that V improve detection accuracy that often surpasses state-of-the-an (SoTA) methods.

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Distance Curvature (*)
Pixel Domain
Representation Domain
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53d
HumanV'S inspired
MVT
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VGS
5000
500
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SIN-50
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BYOL
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DINOV
CUP
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Al-Generated
-20
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40
(A)
A Curvature (0GenA) BNac)
(B)
Figure 2: (A) In pixel space (left), video trajectory metrics (curvature, distance; see Eq. for details) between natural vs. Al-generated videos show substantial overlap. In contrast, DINOv2 representations (right) straighten natural trajectories, clearly separating natural and Al-generated videos. (B) The mean curvature gap (Af) between Al-generated and natural videos across various visual encoders. HVS-inspired models (red) exhibit negative deltas, straightening both natural and Al videos equally, while SSL models (green), particularly DINOv2, show the largest positive deltas.
2 Related work: detecting Al-generated videos
Detecting AL- Al-generated video (see Appendix for an Al generative models overview) becoming in- creasingly challenging. Many early detection efforts, including image-based detectors (CNNSpot (27), Fusing 28) Gram-Net (16), FreDect [29]. GIA (30, LNP (31). DFD [32), UnivED (3)), focused on spatial or frequency-domatt artifacts within individual frames. However, their frame-centric nature limits their efficacy on videos, where temporal consistency is paramount.
Dedicated video detectors, such adapted action recognition models (TSM [34, I3D [35]. Slow- Fast (36]) and Transformer-base (X3D (37. MVIT-V2 (38] VideoSwin [39] TPN 40). UniFormer- V2 41). tencies. While advancing temporal modeling, they require extensive training and may still struggle across rapidly evolving AI generative models. Those approaches may overlook more fundamental signal: geometric distortions the temporal trajectory of neural representations. We hypothesize that the geometric properties of these rnjeecories-rrffcctinng the inherent smoothness and predictability of natural dynamics that generative models fail to replicate-ofir Unlike related work in video quality assessment that also uses trajectories
TimeSformer [42]. DeMamba 43 aim to learn motion anomalies and temporal inconsis-
a more robust signal for detection.
47 45
our focus is
distinctly on detecting synthetic content, regardless of its perceptual quality.
3 Perceptual straightening definition
Natural input sequences are often highly complex. For instance, even video of a simple object moving across an image will be nontrivial sequence of points traveling through a high dimensional pixel space. Specifically, this sequence will be curved since the only straight video is an interpo- lation between two frames. According to temporal straightening hypothesis, biological visual systems simplify the processing of dynamic stimuli by transforming curved temporal trajectories into straightened trajectories of internal representations [T] 2) Although the raw pixel trajectories of natural videos are highly curved, the neural representations in the human visual system become straightened to support efficient temporal prediction and processing [2]. In this article, we exploit this property to detect differences between Al-generated and natural videos.
Formally, let video segment be represented by temporal sequence of T feature vectors, R is the embedding for the i-th sampled frame (with i being the frame index). The displacement vector between consecutive frame representations defined Az; 1 zi, for 1...., T 1. The magnitude of this displacement, which we term the stepwise distance, d, ||Az| Following 2 the curvature 0, of the represen- tation trajectory is defined as the angle between successive displacement vectors, Az, and Ast1i
Z = (21, .2...., where each

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Curvature
2;41
Consecurive Displacement Consecutive Displacement
41+11
A
ii
0:
arecos
|Azi|l
|
(1)
2i+2
Temporal Distance (d.) Temporal Distance (d:-1)
Figure 3: Trajectory curvature Eq.
Cosine similarity hetween A=; Azt+1
i- i_,,..., T T 2. The curvature 0g (Equation D defined for each discrete step along the trajectory (ranging from to T" - 2, where I' is the total number of sampled frames), is computed from the cosine similarity between successive displacement vectors A2, and A 2 This geometric relationship is visualized in Figure 3 The figure depicts three consecutive frame embeddings (2,, 2,+1 21+2) from the overall dashed trajectory. The orange vectors 22; and A represent the displacements between these embeddings, with their respective lengths being the stepwise distances d, and d The green angle 0g shows the turn at 2 This angle, typically converted to degrees (0.- = 0, 1 provides a measure of how sharply the representation trajectory bends at each step. These metrics, stepwise distance d, and curvature 0,, form the core of our geometric analysis.
4 Perceptual straightening of natural videos in DINOv2
Pixel Domain
Representation Domain
Natural
MS
VC2
Figure 4: t-SNE embeddings of curvature trajectories for 1,000 videos from the VideoProM dataset 3]: 500 natural and 500 Al-generated (125 each from Pika [46). VideoCrafter2 [47), Text2Video- Zero (48), and ModelScope [49]; 24 frames/video). Left (Pixel Space): Natural and synthetic trajectories overlap significantly. Right (DINOv2 ViT-S/14 Representation Space): Trajectories clearly separate, with natural (blue) and Al-generated (shades of red) videos forming distinct clusters.
The classic finding is that visual system model have lower curvature in the representation space compared to pixel space-this is called perceptual straightening 2]. For our application, we want to compare the relative curvature of natural and Al generated videos in any representational space, Just for simplicity, we could call this real straightening as in real videos are less curved than AI videos. One might expect that a model with good perceptual straightening also has good real straightening.
To test this hypothesis. we analyze fourteen vision encoders across diverse families: Supervised CNNs (AlexNet (50). VGG-16
51, ResNet-50 (52 and the texture-debiased SIN-ResNer-50 (53)
Self-Supervised (SimCLR-R500 [54] BYOL-R50 [5 CLIP (56). and DINOv2 25)): Human Visual
System-Inspired (a Gabor Filter Bank (57] and the LGN-VI model M); Spatio (S3d (58).
R(2+1)D [59] and MViT (60); and an Adversarially Trained ResNet-50 61.. Surprisingly, as
shown in Fig. 22B we observe that the opposite seems to be the case: good perceptual straighteners actually make natural videos more curved than AI videos. HVS-inspired models achieve the strongest absolute straightening but do so indiscriminately for both real and Al videos. resulting in negative curvature gap (A0 < 0). In contrast, self superviede models like DINOv2 reduce the curvature of natural videos, which align with their learned priors of real-world statistics, but do not regularize the trajectories of Al-generated videos, which violate these priors. This differential response creates a large, positive curvature gap (AO- = 45.46" for DINOv2), which is the foundation of our method's success. The negligible correlation between absolute straightening and detection capability (p = -0. p 0.64) confirms that detection performance hinges not on absolute straightening capability,

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While artificial neural networks may not fully replicate the absolute perceptual straightening observed in the biological visual system 24 this relative effect is the key mechanism for our detection method.
but on differentially straightening natural versus synthetic videos.
This principle is clearly visualized in Fig. Z Using matched pairs of real videos from Physics-IO I [26] and their Al-generated replicas, we see that geometric metrics overlap considerably in raw pixel space. However, in DINOv2's representation space, the trajectories separate distinctly, offering clean signal for detection. Further evidence is provided in Fig. which shows that this separation holds on a larger diverse dataset (VideoProM Bl) The t-SNE embeddings of curvature trajectories show natural videos (blue) forming tight cluster, well-separated from the clusters of Al Al-generated videos (shades of red). This demonstrates that DINOv2's features effectively surface temporal inconsistencies without any task task-specific training. Refer to Appendix A. for trajectories samples.
To implement this, we extract T 24 frames 2 (21...., #24) by sampling over 2-second video duration. This 2-second window is suitable for the videos considered in Section 7 ( 2 - 5s long with 12 30 FPS), with an temporal based sample frame of At 2s/ (24 I). Nevertheless, we hypothesize that using longer videos could further enhance performance. Our choice of 2s window with 24 frames was found to provide an optimal trade-off between high detection accuracy and computational efficiency, as validated in our ablation studies (see B for details). Each x; is resized to 224 x 224 pixels and normalized to [0. 1]. These preprocessed frames are then encoded by the DINOv2 ViT-S/14 model [25]. The 384 CLS (Classify) tokens and 196 patch embedding (16 # 16 patches of the 224+ * 224 inputs) from its final transformer block (block.11). These token embeddings are then flattened and concatenated to form a single feature vector E IR 756-18,, The sequence of these vectors, 2, forms the temporal trajectory in DINOv2's representation space, from which temporal curvature and distance metrics are computed (Eq. (T).
Takeaway 1:
By projecting videos into DINOv2's representation space, geometric trajectory features (curvature & distance, Eq. [ become indicators of synthetic origin, differentiating Al-generated videos from natural ones in a way that is not possible in raw pixel space.
5 Analyzing characteristics of perceptual trajectories
In order to analyze the differences of natural video trajectory signals vs. Al-generated ones we defined statistical features as the first four descriptive moments of both distance and cur- vature: mean, minimum, maximum and variance. This yields an 8-dimensional feature vector: [Ha, min d, max d, o² Pe, min 0, max 0, oz]. where μaa = - Σ, d; and a3 L. 22) (analogously for curvature 09).
We select 50,000 Al-generated samples (10,000 each from Pika 46 VideoCraft2 62 Vide2
Zero (63), ModelScope R and Sora (64) from VideoProM 3. Concurrently, 50,000 natural videos are randomly chosen from DVSC2023 [47. All videos are DINOV2 (ViT-S/14) encoded, and their aggregated statistical features are computed. Fig. Fillustrate these aggregated feature distributions. The top row shows distance features (Ma, mina, maxa, oz), characterizing inter-frame change magni- tude and variability. The bottom row presents corresponding curvature features (Hz, ming, max,, of), reflecting angular changes between consecutive frame transitions.
Statistical tests further confirm these observed differences. two-sample t-test comparing the mean per-video μ between natural and Al-generated videos produced highly significant results. Distance 46, Curvature (0): t = -4..02, Re O. An ANOVA comparing
(d): t -14.27, p = 5.53 x 10 10-46, feature distributions among different AI generators and natural videos also shows strong statistical differentiation in DINOv2 embedding space (a'-value of 18598.17, p RS 0). These observations support hypothesis: natural videos exhibit smoother, more consistent trajectories (lower 14g surprisingly higher oi), while Al-generated videos show irregular transitions resulting in higher curvature metrics but with lower oz. These differences form the basis for our classification pipeline, where classifier learns to separate natural from AI videos based on their trajectory geometry.
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Figure 5: Distributions of aggregated temporal trajectory features (mean, min, max, variance) for natural and Al Al-generated videos, computed using DINOv2 ViT-S/14 representations. Top row: Temporal distance-based features (d,). Bottom row: Corresponding curvature-based features (f.). Both distance- and curvature-bass features provide discriminative signal.
Video classifier to detect Al-generated content
Given that DINOv2 representation distance d and curvature differs significantly between natural and Al-generated videos, evaluate if these features can be used in a lightweight, transparent, and easily replicated classifier without raw pixel processing or DINOv2 fine-tuning. We use the dataset from Section and apply stratified 50/50 train/test split. Class priors are identical, and each subset balanced among five Al models (Pika (46), VideoCraft2 (62) Texx22ideo-7ae [63], ModelScope
B and Sora (64] ). We sample frames and we obtain the signals of distance { and curvature {0:) as detailed in Section 5 For classification, we construct feature vector per video by combining direct signals and aggregated statistics from these trajectories. Specifically, y concatenates seven distance values [d1, d2..... d7] and six curvature values: [0g 05 ....... .g]; and four statistical descriptors (mean, variance, minimum, maximum) for both {d.} and {0 This results in a final feature vector 2 To ensure our curvature-bacc features are detecting generative artifacts rather than hard scene cut frequency. we performed a robustness analysis detailed in Appendix [
We consider only off-the-shelf models: logistic regression (LR), Gaussian Naive Bayes (GNB), random forest (RF; 400 trees, depth S 6), gradient boosting (GB; 200 rounds, learning rate 0.1), RBF-kernel SVM (calibrated by Platt scaling), and a two-layer MLP (64 - 32). We perform no feature engineering or hyperparameter search beyond a 3-fold grid/random sweep. For each classifier, we optimize the decision threshold -* on the training set to maximize the Fi-score. The chosen threshold 7" was then applied unchanged to the test set. Inference cost reported end-to- end (latency = TDINOv2 + Ter), averaged over the test fold on single NVIDIA RTX-2080 (see Appendix D). DINOv2 forward pass (ViT-S/14, block 11, 8-frame batch) takes 43.6 ms. This constant is added to each classifier time (Ten) in Table
Table 1: Performance and inference time of ReStraV's classifiers, bal- anced 50k/50k natural/AI-generea
MLP
98
AUROC (96)
video test set from VideoProM (3), ef. Section for details. The best scores are bold, second best are underlined and the best method overall is highlighted in blue.
Acc, (%)
GB
96
90
GNB
94
Model
Acc.
Bal.
Spec.
Prgen
Regen
FI gen
AUROC
Time (ms)
92
10
10a
SVM
85.23
85.78
86.42
96.93
85.04
90.62
93.27
1183.94
ms per video
GNB
86.64
84.43
81.12
95.94
87.72
91.68
92.05
44.53
Figure 6: Inference-time (ms) vs. accuracy (%) and AUROC (%) for ReStraV's classifiers.
LR
89.02
88,86
88.53
97.54
89.17
93.12
95.26
43.97
GB
92.83
92.31
91.57
98.25
93.16
95.63
97.85
48.59
RF
94.24
88.67
80.37
96.13
97.05
96.53
98.03
48.14
MLP
94.17
94.19
94.11
98.88
94.14
96.48
98.63
48.12
Table summarizes test performance. The MLP achieves the highest accuracy (94.17%), Fi (96.48% 48%) and AUROC (98.6%) (Fig. AA), followed by RF. Section visualizes each classifier's speed/accuracy. Models in the upper-left offer the best cost/benefit. The MIP (highlighted blue Table M achieves top AUROC and F, while heing within 222ms from GNB. The confusion matrices and ROC (Fig. 9A and B in Appendix A.2 confirms low false positive/negative rates (cf.

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Appendix A.2 for decision boundaries visualization). Refer to Appendix A.3 for permutation feature importance analysis.
Simple, lightweight classifiers trained directly on geometric trajectory features achieve high classification accuracy (+= 94%) and AUROC (= 99%), offering an efficient detection approach (=s 48 ms per video) without complex modeling.
Takeaway 2:
7 Benchmark results
We evaluate ReStraV (with MLP from Section 6 under four settings: A) Vs. SoTA image-based detectors on VidProM 3: B) Vs. SoTA video detectors on VidPro. [3 (evaluating "seen", "unseen", and "future" generator scenarios) and on the Gen VidBench [1 dataset (in "cross-source" """urcce""geeeratoo"" (M) scenarios and its "Plants" hard task subclass (P)); C) Extreme generalization tests including one-to-many detection
the DeMamba [43] benchmark 43 and D) zero-shot evaluation on the Veo3 model [65]; E) Vs. a Vision-Language Model (VLM) performance (Gemini 1.5 Pro (66)) on the Physics-IQ dataset (26] using matched real/generated video pairs. The best scores are bold, second best are underlined and the best method overall is highlighted in blue.
A) ReStraV vs. image-based detectors. We evaluate our method ReStraV against eight SoTA image based detectors Table 2 ReStraV is trained with data and processing from Section 66 use balanced test set (40,000 real videos; 10,000 Al-generated for each of four models: Pika 46
We
VideoCraft2 667 Text2Video-Zenu [48] ModelScope [49
replicating
3 's implementation.
Performance is measured by overall classification accuracy and mean Average Precision (mAP). Table summarizes the results from 33. Bascline methods achieve moderate accuracies (45%-62%) with LNP [31] and Fusing (28) showing lower values. In contrast, our method obtains 97.06% average accuracy (Pika: 90.90%, VideoCraft2: 99. 99.50%, Text2Video-Zee video task is not an even comparison (see next section for stronger baselines), yet highlights the inadequacy of methods that rely solely on Image-based features for Al-generated video detection neglecting temporal information.
99. 9905%, ModelScope: 98.37%).
Caveat: Comparing ReStraV to mmage-based detectors
Table 2: Comparison of V vs. image-based detectors on VidProM 3 Accuracy (%) (left) and
mAP (%) (right). Higher values (darker blue) indicate better performance. Higher is better.
Accuracy (%)
mAp
(%)
Method
Pika
VC2
T2VZ
MS
Avg
Pika
VC2
T2VZ
MS
Avg
CNNSpot 27
51.17
50.18
49.97
50.31
50.41
54.63
41.12
44.56
46.95
46,82
FreDect
29
50.07
54.03
69.88
69.94
60.98
47.82
56.67
75.31
64.15
60.99
Fusing
28
50.60
50.07
49.81
51.28
50.44
57.64
41.64
40.51
56.09
48.97
Gram-Net
16
84.19
67.42
52.48
50.46
63.64
94.32
80.72
57.73
43.54
69.08
GLA 30
53.73
51.75
41.05
60.22
51.69
54.49
53.21
36.69
66.53
52.73
LNP 31
43.48
45.10
47.50
45.21
45.32
44.28
44.08
46.81
39.62
43.70
DFD 32
50.53
49.95
48.96
48.32
49.44
49.21
50.44
44.52
48.64
48.20
UnivFD
49.41
48.65
49.58
57.43
51.27
48.63
42.36
48.46
70.75
52.55
ReStraV
90.90
99.50
99.05
98.37
97.06
99.12
98.76
98.93
98.44
98.81
B) ReStraV vs. video-based detectors. We firstly compare ReStraV against the widely recognized VideoSwin [39] (implementation from (17) the VidProM B3. with data setup following Section 7 Our evaluation considers three scenarios: Seen generators: Models are trained and tested on videos from pool of the same four AI generators in Section using a balanced set of 80,000 videos with a 50/50 train/test split. Unseen generators: Generalization is assessed by training models while excluding two specific AI generators (e.g., VC2 [62] and T2VZ 48).. which are then used for testing. Future generators: To simulate encountering a novel advanced model, ReStra (trained on older generators) is tested on Sora (68 Accuracy and mAP results in Table 3
We futher evaluate ReStraV's vs. nine SoTA video based detectors in Table 4 We consider two settings: Main (M) task, which is designed to test generalization across generators. Detectors are
7

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trained on videos from Pika 46], VideoCraft2 [67] Text2Video- 48 ModelScope 49 and tested on MuseV 669, Stable Video Diffusion (SVD) 70. CogVideo 71, and Mora 72 Plants (P) task, the most challenging subset from [7 The challenge may arise from the complex and often stochastic nature (e.g., irregular leaf patterns, subtle wind movements), which can make generative artifacts less distinguishable from natural variations or harder for models to consistently detect (qualitative samples in Appendix[A.4] We use the same setting of task (M) but focusing on videos of plants in the test set. Baseline's ' results from [17].
Across both the Main (M) and Plants (P) tasks, Re- StraV consistently performs near or above the base- line. On the Main (M) task, it demonstrates strong accuracies against AI generators (MuseV 98.52 52,, SVD 94.01 %, Cog Video 93.52%, Mora 92.97%) and robust performance on natural videos 3H/3VG/130M 91.07%), achieving a 93.01% average accuracy.
Table 3: ReStraV vs. VideoSwin 33 fake video detection on VidProMM[]. "Seen gener- ators" are those included in training; "Unseen generators" and "Future generators" were ex- cluded from training. f is better.
VideoSwin ReStra [39]
Condition
(MLP)
This robustness extends to the challenging Plants (P) task, where ReStraV obtains accuracies of 95.06% (MuseV), 97.83% (SVD), 92.38% (CogVideo) 91.24% (Mora), and 93.31% 3G/-30G)) lead- ing to a 93.96% average. This success may be at- tributed to ReStraV's ability to capture specific curva- ture patterns inherent to "Plants" videos, which differ from more general artifacts. ReStraV remains highly effective as visualized by its position relative to the baseline spread (Fig. 13 in Appendix A.5)
Ace: 77.91
97.05
Seen generators
mAP: 75.33
98.78
Unseen generators
Acc: 62.44
89.45
662 63
mAP: 59.61
97.32
Future generators
Acc: 60.70
80.05
(Sora (68)
mAP: 58.20
92.85
Table 4: Acc. (%) results of ReStraV SoTA video based methods on the VidBenhh 17
Table
(a) shows results for the Main (M) task, and Table (b) for the Plants (P) task. 1 is better.
(a) VidBeech Main (M) Task Acc. (%)
(b) Gen VidBench Plants (P) Task Acc. (%)
Method
MuseV
CogVideo
Avg.
Method
MuseV SVD Cog Video Mora HD-VG
Avg.
[Nat.]
[Nat.]
TSM
70.37
54.70
78.46
70.37
96.76
76.40
SlowFast
26
81.63
29.80
75.31
19.31
73.03
55.30
X3D
37
92.39
37.27
65.72
49.60
97.51
77.09
BDD 35
39.18
23.27
91.98
78.38
78.12
62.15
MVIT
76.34
98.29
47.50
97.58
79.90
VidenSwin
57.96
92.59
98.76
SlowFast
36
12.25
12.68
38.34
45.93
93.63
41.66
TPN 40
43.67
20.00
85. 50.80
86.87
94.61
64.24
BD
35
8.15
8.29
60.11
59.24
93.99
49.23
UniFomner V2 41
13.88
7.76
41.98
95.75
97.93
61.76
Wid
62.29
8.01
91.82
45.83
99.29
67.27
TimeSformer
93.44
75.09
ReStraV
93.52
93.52
93.01
ReStraV
95.06
97.83
92, 38
91.24
93.31
96.96
43 C) One-to-many generalization Test. We reproduced the one-to-many task from DeMamba
training on a single generator and testing on multiple unseen ones (Sora [68] MorphStudio
73
Gen2 (74), HotShot [75]. Lavie [5). Show (76). Moon Valley (77), Crafter [67]. ModelScope (49 and WildScrape(43))
Table [ shows average results across three training conditions. Notably, ReStraV achieves competitive superior scores in most scenarios against specialized video detectors
(TALL 78), NPR 77), STIL 80) and DeMamba
4 using only the extracted trajectories.
D) Zero-shot Generaliza- tion Test. We tested zero-shot generalization on Google's Veo3 (65) a state- of-the-art model acclaimed for its ability to generate videos with plausible phys- ical interations and con- sistent object interactions (qualitative frame samples Appendix (A66). Us- ing only the MIP trained in Section (without any Veo3 videos in training), we
Table 5: One-to-many training on one generator, testing on unseen generators (Sora, MorphStudio, Gen2, HotShot, Lavie, Show-1, Moon- Valley, Crafter, ModelScope, WildScrape). Avarage results from De- Mamba benchmark
4
is better.
Train: Pika
Train: SEINE
Train: OpenSora
Method
R
F1
AP
R
Fl
AP
R
F1
AP
NPR
0.514 0,531 0.650
0.462
0.539 0.611
0.593
0.523
0.576
STHI
0.738
0.517 0.630
0.724
0.506 0.608
0.434
0.489
0.526
TALL
0.714
0.557 0.623
0.657
0.609
0.681
0.492
0.532
0.571
DeMamba
0.757
0.726 0.817
0.810
0.787 0.894
0.738
0.671
0.738
ReStraV 0.735 0.827 0.797
0.820
0.898
0.854
0.771
0.797
0.717
8

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tested on 200 Veo3 versus 200 natural video pairs and achieved 83.2% accuracy. 85.1% FI, and 86.9% AUROC. This shows that the curvature-baae detection signal can generalizes to future generators not represented in the training distribution, supporting our hypothesis that current generative models fundamentally struggle replicate the temporal smoothness characteristic natural world dynamics in learned representation spaces.
E) V vs. VLM detector Physics-IQ dataset (matched real and generated videos).
100%
2AFC Acc. (%)
As third and perhaps the most challenging test, we assess ReStraV on matched pairs natu- ral and generated videos from the Physics-IQ dataset. This dataset consists of real-world phys- ical interactions and special in the sense that consists of both natural and Al Al-generated videos (198 per source) that are based on the very same starting frame(s): identical scenes. identical ob- jects, identical lighting conditions as described in Section We report [26] 's evaluation using wo-aalernative forced-choice (2AFC) paradigm (a gold- gold-standard psychophysical protocol). In each trial, model sees a pair of videos: one real, one Al-generated.
80%
60%
50%
Chance
Runaway
Pika
Sora
Gemini 1.5 Pro
ReStraV
Figure 7: Fake video detection Physics-IQ (matched real/generated video pairs). Gemini re- sults from [26). Despite the challenging task, Re- StraV reliably identifies fake videos.
The task of identifying the AI-generated video is especially challenging due to the matched video nature. Motamed et al. [26) report results for VLM, Gemini 1.5 Pro [66]. Gemini identifies Runway and Pika videos with reasonable accuracy (74.8% and 80.5% respectively), but Sora videos prove challenging (55.6%, near 50% chance) due to their photorealism.
We evaluate ReStraV in the same setting, comparing against reported numbers 26 We compute mean aggregated video curvature (Eq. (T)) for each video and predict the one with higher mean curvature as "Al-generated." No further classifier training or calibration is performed. Fig. 7 shows the results: ReStraV attains 97.5% for Pika [46). 94.9% for Runway [81), and 99.0% for Sora. This near-perfect performance across all three generators demonstrates that simple curvature statistics robustly discriminate real from generated videos without model fine-tuning.
ReStraV demonstrates robust generalization across diverse generators and OOD scenarios, showing neural representation trajectories (distance d and curvature 0) as effective paradigm for AI video detection.
Takeaway 3:
Discussion
Summary. As Al- Ae-gerrrteed videos look more and more realistic, increasingly important to develop methods that reliably detect Al-generated content. We here propose using simple statistics such as angle between video frame representations, inspired by perceptual straightening hypothesis from neuroscience [I, to distinguish natural from generated videos. The approach is compellingly simple, fast, cheap, and surprisingly effective: using pre-trained feature space such as DinoV2, the resulting "fake video" signal reliably identifies generated videos with high accuracies, setting a new SoTA fake video identification.
The surprising observation that natural videos have, average, less curvature but at the same time higher variance in their curvature demands attention. Prior work found that temporal transitions in natural videos latent representations follow highly sparse distributions [82]. That means most of the time there very little change, but sometimes a large jump. In terms of curvature, this could mean that most of the time, natural videos follow relatively straight line through representation space, but sometimes take sharp term (perhaps a scene cut). Further investigation trajectory geometry (e.g.. curvature kurtosis) will help to shed light on this in future work.

---

Implications for neuroscience. The finding that natural videos trace straighter paths than Al- generated ones in a frozen vision transformer dovetails with the perceptual straightening phenomenon reported in perceptual decision tasks and brain recordings I 2]. In the brain, such straightening is often interpreted as a by-product of predictive coding: when the visual system internalizes the physical regularities of its environment, successive latent states become easier to extrapolate, reducing curvature in representation space. Our results imply that even task-agnostic, self-supervised networks acquire comparable inductive bias Suggestggn a shared computational pressure, across biological and artificial systems, to encode "intuitive physics" in a geometry that favors smooth trajectories 83
Naturalistic straightening offers a concrete, quantitative handle for probing world-mode model formation in neural populations. Future work could ask whether curvature statistics in cortical population codes track the degree of physical realism in controlled stimuli, or whether manipulations that disrupt intuitive physics (e.g., gravity-defying motion) elicit the same curvature inflation we observe in synthetic videos. Such experiments would clarify whether the brain genuinely leverages trajectory geometry as an error-monitoring signal and how this relates to theories of disentangled, factorized latent representations of dynamics.
Our method invariant playing video backwards. This is clearly unnatural, if things, e.g., fall up instead of down; though at the same time this also would not be an instance of an Al-generated video. The arrow of time 84 86 can be a strong signal, but our metric is invariant to a reversal of time.
Limitations. Goodhart's law states that "when a measure becomes a target, it ceases to be good measure". Likewise, in the context of fake video detection, it is conceivable that someone developing video model could train it in a way that optimizes for deceiving detection measures. This concern generally applies to all public detection methods. including ours. As a possible mitigation strategy, may be helpful to employ several detection methods in tandem, since may be harder to game multiple metrics simultaneously without sacrificing video quality. Furthermore, as video models become more and more capable of generating realistic, natural-looking videos, it is possible that future video models may not show the same statistical discrepancies between real and generated videos anymore, though this is hard to predict in advance.
Broader Impacts Al-generated video increasingly fuels fake news and disinformation [15] Re-
StraV aims to positively impact this by enhancing content authentication. With AI-driven fraud like deepfake scams reportedly surging (c.g., a reported 2137% rise in financial sector attempts over three years (87), efficient detection methods like ReStraV V are becoming fundamental.
However, deploying detection technologies like ReStraV faces an "arms race" with evolving genera- tion methods (see Limitations; also (22). Additionally, biases inherited from pre- tre-trained encoders (e.g., DINOv2 (25) may cause fairness issues across diverse content [8] Mitigating these risks demands ongoing research, transparency about limitations, and using detectors primarily to aid human judgment. Key strategies include careful contextual deployment, rigorous bias auditing and debiasing efforts [88] promoting media literacy [15], and advancing complementary methods like
robust content watermarking (20].
The importance of Al-safety measures 189 is increasingly reflected in policy initiatives like the Coalition for Content Provenance and Authenticity (00 and the EU AI Act (9T), both vital for trustworthy digital ecosystem. However, deploying detection tools at scale presents its own challenges, especially concerning user privacy. To address this, detectors can be distributed and trained using grivacy-by-desipe principles [92 (4). Within this framework, tools like ReStraV are crucial for ensuring the digital ecosystem remains grounded in reality, providing a critical defense against the long-term risk of epistemic decay in world models [95] 96
Acknowledgements
This research was partly funded by Honda Research Institute Europe and Cold Spring Harbor Laboratory. We would like to thank Eero Simoncelli for insightful discussions and feedback, as well as Habon Issa, Filip Vercruysse, CiCi Xingyu Zheng, Alexei Koulakov, Andrea Castellani, Sebastian Schmitt, Andrea Moleri, Xavier Bonet-Monroig. Linus Ekstrøm, Riza Velioglu, Riccardo Cadei, and Christopher Van Buren for their helpful suggestions during the preparation of this manuscript, and Elena Ziegler for the appendix art illustration.
10

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Appendix Contents
A
AI Video Generation
20
A.1
Qualitative Examples of Perceptual Trajectories
20
A.2
ReStraV Classifiers Results Analysis
Feature Importance Analysis
22
A.4
Qualitative Examples of "Plants" "
Task
A.5
Results Distributions for Main Task (M) and Plants Task (P)
A.6
Frame Samples from the Veo3 Model for Zero-shot Generalization Test
Ablation Studies
B.1
Impact of Video Length and Sampling Density
B.2 Robustness to Temporal Window Position
24
Analysis of Scene Cut Frequency
26
Computational Environment
26
Dataset I Licenses and Sources
26
?
Eur
19

---

A AI Video Generation
Early video generation used deep generative models like GANs and variational methods. [97] used VGANs for tiny video loops; [7] introduced MoCoGAN to separate motion and content. These pioneering methods, despite enabling synthetic video generation, often produced blurry or temporally incoherent results. [98] addressed future frame uncertainty with the Stochastic Variational Video
Prediction (SV2P) model, using stochastic latent variables for diverse video sequences.
Diffusion models marked a significant breakthrough. Foundational methods like DDPM 12 (images) and Latent Diffusion [99] achieved high-fidelity generation via iterative denoising. Video Diffusion Models (VDMs) then addressed temporal consistency, e.g.. using time-conditioned 3D U-Nets [00]
This led to prominent text-to-video systems like Imagen Video [100] and Make-A- --Vidoo 48 often using cascaded super-resolution. Latent diffusion variants like Text2 Eex22Viee-Zern [63] and ModelScope improved efficiency by operating in latent spaces. Generative foundation models have diversified beyond diffusion. OpenAI's Sora [64] showed strong text-to-vided capabilities using transformer decoders. Runway Gen-3 [8] uses autoregressive generation for temporal dynamics; Pika [46 combines diffusion and autoregressive decoding for improved coherence and quality.
Despite these advances, robust temporal consistency and physical plausibility remain significant challenges. Temporal inconsistencies occur even in sophisticated models like Stable Video Diffusion
(SVD) 70 [OT]. Even top models like Sora [64] and Google's VideoPoet [102] show coherence
issues or generate implausible scenarios [26]. A critical gap is the lack of explicit physical dynamics modeling and coherent scene understanding, leading to unrealistic motion and interactions (26). Incorporating world models to learn physical principles and causality [95 [03) is a promising research direction. These could mitigate temporal inconsistencies by enforeing structured scene understanding and dynamic constraints [[03] [04]]. Motivated by these persistent limitations in achieving temporal and physical realism, our work proposes detection methods that exploit subtle
irregularities in the geometric properties of neural representations 2
A.1
Qualitative Examples of Perceptual Trajectories
Natural
Sora 0-
GenAi
100
120
pika
100
80
120
runwaye-
100
80
SP
6 Por S Normalized Time
S Normalized Time
P P Ma Normalized Time
Normalized Time
Normalized Time
Figure 8: Examples raw curvature trajectories (0,) over normalized time for pairs of natural videos (blue) and Al-generated vidcos (red) from different generative models (Sora, Pika, Runway). Each column represents a different example pair. These qualitative examples illustrate the tendency for Al-generated videos exhibit different curvature patterns, with more erratic fluctuations compared to their natural counterparts when viewed in the DINOv2 representation space.
To provide more intuitive understanding of how curvature trajectories differ, Figure g presents several qualitative examples. Fach plot shows the sequence of calculated curvature values (0, for natural video (blue line) and corresponding Al-generated video (red line) from the Sora 68 Pika
466, or Runway [8I] models, after processing through the DINOv2 encoder as in Section
20

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While individual trajectories can be noisy, these examples visually highlight common tendencies observed: Al-generated videos frequently display trajectories with different overall levels of curvature, more pronounced peaks, or more erratic behavior compared to the often smoother or distinctly patterned trajectories of natural videos.
A.2
ReStraV Classifiers Results Analysis
True Positive Rate
RF
GB
SVM
1.0
Nat 76.0%.4.0%
91.5% 8.6%
0.5
True label
GenAl
7.2%
15.4% 44.6%%
0.0
MLP
LR
GNB
0.0
0.5
1.0
Nat-3 93.3% 6.7%
90.0%10.09%
85. 14.9%
False Positive Rate
RF (0.98)
MLP (0.99)
GenAl 5.5%
94.5%
11.7%
15.4% 94.6%%
GB (0.98)
LR (0.95)
SVM (0.93)
GNB (0.92)
Nat GenAl
Nat GenAl
Nat GenAl
Predicted label
(A)
(B)
Figure 9: (A) ROC curves for various classifiers (Logistic Regression, Gaussian Naive Bayes, Random Forest, Gradient Boosting, SVM, MIP) on test set. The MI.P achieve the highest AUROC. (B) Normalized confusion matrix for the ReStraV classifiers on the test set, illustrating rates for both natural (Nat) and Al-generated (GenAl) classes. Values are percentages.
Figure 9A presents the ROC curves for all classifiers detailed in Table T The curve for the MLP VReStraV) is closest to the top-left corner and with the largest area, AUROC (98.63%) Figure 9 displays the normalized confusion matrices for the ReStraV classifiers from Section 6 The strong diagonal elements (e.g., correctly identifying natural videos as "Nat" and Al-generated as "GenAl") and low off-diagonal values highlight the effectiveness. Specifically, correctly classifies a high percentage of both natural and Al-generated instances, aligning with the balanced accuracy and individual precissin/reeall/speeiiie metrics reported in Table
LR Decision Regions
MLP Decision Regions
50
50
100
50
Natural
AL-Generated
Figure 10: Decision boundaries for Logistic Regression (LR) and Multi-Layer Perceptron (MLP) ResTraV's classifiers. The plots illustrate how these models partition a 2D projection of the feature space (detailed in Section 6) to distinguish between natural (blue circles) and Al-generated (red triangles) videos. This comparison highlights the different decision boundaries learned by linear (LR) and non-linear (MLP) model when applied to the geometric trajectory features.
For demonstration purpose, we construct Voronoi tessellations to visualize the decision boundaries obtained from two different classification models: Logistic Regression (LR) and Multi Layer Preceptor (MLP) classifier from Section o
in Fig I0 The visualization underscores the benefit of
21

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using non-linear classifier for this task, given the nature of the feature space derived from StSav''s geometric analysis.
A.3 Feature Importance Analysis
Hg
mi mm
max:
Aggregated Statistics
ming
minp
05
87
07
Individual Time-Step Signals
81
Curvature Feature Distance Feature
Fi-score Drop (%)
Figure 11: Permutation Feature Importance. The plot shows the drop in Fi-score (%) when each feature is permuted, with error bars indicating standard deviation. Features are grouped into "Aggregated Statistics" (c.g., mean, variance) and "Individual Time-Step Signals" (e.g., do, f1). The results highlight the overwhelming importance of the mean curvature (140) and other aggregated statistics distinguishing natural from Al-generated videos.
To identify which of the 21 features contributed most to the performance of our best classifier (the MLP), we conducted a permutation feature importance analysis. We trained the MLP on the 50,000 Al-generated and 50,000 natural videos described in Section 5 and then measured the drop in Fl-score when each feature was individually shuffled. A larger drop indicates a more important feature. The results are visualized in Fig.
The mean curvature (ug) unequivocally the most critical feature. Its importance is more than double that of the next most influential feature, the curvature variance (az)) This provides strong quantitative evidence that the overall "straightness" of trajectory the primary signal our method leverages. The eight aggregated statistical features occupy the top tiers of importance, In contrast, the features representing individual, time-spee specifie distance and curvature values (d,, 02) have a much smaller impact, suggesting they provide complementary but less critical information. These findings validate the distributions shown in Figure where the aggregated statisties for curvature and distance showed the clearest separation between natural and Al-generated videos. The classifier effectively learns to exploit these high-level geometric properties.
A.4 Qualitative Examples of "Plants" Task
Natural (HD-VV/130M)
Mora
MuseV
SVD
CogVideo
Figure 12: Sample from the Gen Gen "Plants" task [17 for natural video (ID-VG/130M)
and Al-generated plant video frames ( 69), SVD [70), Cog Video [71), and Mora 722
22

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shows qualitive samples of "Plants" task (P) [17] This task involves videos where the primary subject matter is various types of flora. Natural videos VG/-30M0 (T00) exhibit typical characteristics of real-world plant footage. The Al-generated examples from MuseV 69 SVD 770,
Figure 12
Cog Video [7], and Mora 72) showcase the capabilities of these models in synthesizing plant-related content. While visually plausible, these Al-generated videos contain subtle temporal unnatural patterns (e.g., texture evolution) that ReStraV detect through its geometric trajectory analysis. The performance of R this hard task are described in Table
A.5
Results Distributions for Main Task (M) and Plants Task (P)
Main Task (M)
Plants Task (P)
100
*
Accuracy (%)
50
ReStraV (MLP)
0
MuseV (M)
SVD (M)
CogVideo (M)
Mora (M)
Natural (M)
Musev (P)
SVD (P)
CogVideo (P)
Mora (P)
Natural (P)
Figure 13: Accuracy distributions of ReStraV (MLP) and SoTA methods on Gen VidBench [7]. Box- plots summarize performance on the Main task (red distributions) and Plants task (green distributions) across the different generative models within each task.
Figure 13 visualizes the accuracy distributions of various detectors, including ReStraV (MLP), on the Gen VidBench Main (red distributions) and Plants (green distributions) tasks. The boxplots illustrate the median accuracy, interquartile range (IQR), and overall spread of performance for each method across the different generative models within each task. ReStraV (MLP) consistently positioned at the higher end of the accuracy spectrum for both tasks, indicating more stable performance across different generators. For the Main task, ReStraV's median and overall distribution are visibly superior. For the Plants task, ReStraV again demonstrates leading performances. This visualization complements Table by providing a overview of performance consistency and superiority.
A.6 Frame Samples from the Veo3 Model for Zero-shot Generalization Test
To provide qualitative sense of the videos used in our zero-shot generalization test (Section Paragraph D), Figure 14 presents sample frames generated by the Veo3 model. These examples illustrate the high quality of the content our method was tested against without any prior training on this specific generator.
B Ablation Studies
We performed ablation studies understand the impact of key frame sampling parameters on the performance and efficiency of ReStraV, We randomly selected 10,000 Al-generated videos by Sora 688 from VidProM (3] and 10,000 natural videos from DVSC2023 [47] The Sora 68 videos, with longer lengths (5s) and high frame rate (30 FPS as the natural videos), provide robust basis for evaluating wide range of sampling parameters, making them good representative case for the Al-generated set. Performance is evaluated using Accuracy (%). AUROC (%), and F Score (%), with inference time measured in milliseconds (ms). The shaded regions in the plots represent standard deviation around the mean, based on multiple runs involving different random video samples and 50/50 train-test, partitioning. We use the best performer classifier (two-layer ML. (64 - 32)) from Section 6 of the main paper.
23

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Figure 14: Qualitative frame examples from the Veo3 [65] model. These images showcase the high-fidelity content used for the zero-shot generalization test.
B.1 Impact of Video Length and Sampling Density
We investigated how the length of the video analyzed and the density of frame sampling within fixed window affect ReStraV's performance. The results shown in Supplementary Figure I5
Panel (a) Supplementary Figure 15 illustrates the effect of varying the analyzed video length from to 5 seconds, from which ReStraV V processes 2-second segment by sampling every 3rd
frame (at 30 FPS). This results in the number of frames (7') input to DINOv2 [25] being 10, 20, 30, 40, and 50 for the respective conditions. As shown, performance metrics (Accuracy, AUROC, and Fi score for Al-generated content) improve as the analyzed video length increases, with AUROC exceeding 96% for 2-second segments (T 20) and reaching approximately 98% for 5-second segments (T = 50). Inference time increases linearly with T. The 2-second segment analysis, as used in main paper (where T 24 frames processed), offers a strong balance between high performance and computational efficiency (observed around 40-48ms in related tests).
Panels (b) and (c) of Supplementary Figure show the sampling density within a fixed 2-second source video (30 FPS, 60 total frames). Panel (b) shows performance against the sampling interval h (where every th frame is taken). The results indicate optimal performance when frames), achieving an AUROC of ss 97%. Performance degrades for sparser sampling (e.g., k 5, T= 12) and also for very dense sampling (e.g., k 1, = 60). Panel (c) plots performance directly against the number of sampled frames T, confirming peak performance at T 20.
(T' = 20
B.2 Robustness to Temporal Window Position
We also studied the impact of the starting position of the 2-second window when applied to 5s videos. A 2-second window (processed with ReStraV's standard 7 = 24 frames) was slid across 5-second video with a step of 10 frames (approximately 0.33s at 30 FPS).
As shown in Supplementary Figure 16 the detection performance remains largely robust regardless of the window's 's start time. slight U-shaped trend observed, with marginally higher performance at the beginning and end of the analyzed 0-3s window start time range, and a minor dip when the

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2.33 2.67 3.00 (a) Effect of Video Length
100
100
Performance (%)
95
80
Time (ms)
60
40
20
Video Length (s)
(b) Effect of Sampling Interval (k)
(c) Effect of Sampled Frames (T)
120
Performance (%6)
100
80
Time
60
40
20
3
5
1215 20
30
60
Sampling Interval (every k frame)
Number of Sampled Frames (T)
Accuracy
AUROC
- F1 Score
Time (ms)
Figure 15: Ablation on Video Length, Sampling Interval, and Number of Frames. (a) Effect of analyzed video length. (b) Effect of sampling interval (k) for 2s video. (c) Effect of total sampled frames (I) for 2s video, derived from varying k.
Performance (2s Window, 10-Frame Step)
98
Performance (%)
94
92
0.00
0.33 0.67 1.00 1.33 1.67 2.00
Sliding Window Start Time (s)
Accuracy
AUROC
F1 Score
Figure 16: Ablation Study on Sliding Window Start Time. Performance of ReStraV when a 2s window (with T' 24 frames) slides across 5s video with 10-frame step.
25

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window is centered. This demonstrates that ReStraV is not overly sensitive to the precise temporal segment analyzed within a longer video.
C Analysis of Scene Cut Frequency
VC2
Avg. Duration
Avg. Cuts
Natural
Model
(s)
first 2s
Sora
Pika
2.97
0.00
OpenSora
Show-1
3.62
0.00
MorphStudio
ModelScope
3.84
0.00
HotShot
Gen-2
4.00
0.00
T2VZ
HotShot
4,60
0.01
Crafter
5,43
0.00
MoonValley
MoonValley
5.59
0.00
Crafter
T2VZ
6.01
0.00
Gen-2
VC2
6.98
0.10
Al-Generated Models
ModelScope
Natural
7.04
0.08
Natural Videos
Show-1
MorphStudio
7.05
0.02
Natural Video Rate (0.08)
Pika
OpenSora
8.22
0.00
0.02
0.04
0.00
0.s
Sora
12.40
0.04
Average Scene Cuts in First 2s
Figure 17: Analysis of Scene Cut Frequency. The lollipop plot (left) and table (right) show the average number of hard scene cuts within the first 2s for each video source. The dashed blue line in the plot indicates the rate for natural videos. Both visualizations confirm that the scene cut frequency low across all models and not a significant confounding factor.
An important consideration for AI Al-generated video detection is the effect of hard scene cuts, as the straightening hypothesis not expected hold across shot boundaries. This presents potential confound: if Al- Al-generated videos simply contained a higher frequency of scene cuts, it could partly explain our classifier's performance.
To investigate this possibility, we analyzed 13,000 Al-generated and 13,000 natural videos using the "scenedetect" [105] library. The results, presented in Fig. [7] show that the average number of scene cuts is low and comparable for both natural and Al-generated videos. The plot (left) visualizes this comparison, showing all models clustering near the natural video baseline (dashed line), while the table (right) provides the precise data.
short, 2s analysis window inherently reduces the probability of encounter- ing a scene cut. The overall robustness of our approach is further confirmed by the ablation study in Appendix [B.2] [B.2] which shows stable detection performance regardless of the analysis window's temporal position.
Our method's 's reliance
D Computational Environment
All experiments presented this paper were conducted on system equipped with NVIDIA RTX- 2080 GPUs. each with 8GB of VRAM. The feature extraction process using the DINOv2 ViT-S/14 model, which involves forward pass for 24-frame batch, takes approximately 43.6 milliseconds.
E Dataset Licenses and Sources
This dataset was employed for training our video classifier (Section 6 and for benchmarking in Section 7 and Section The VidProM dataset is offered for con-ecmmercial research purposes under CC BY-NC 4.0 license.
VidProM [3]:
Gen VidBench was used for benchmarking Section The GenVid- Bench dataset and its associated code are under CC BY-NC-SA 4.0 license.
Gen VidBench [7]:
26

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This dataset facilitated the evaluation of our method on matched pairs of natural and Al-generated videos that depict physical interactions (Sec. Sec. The Physics-IQ dataset is available under the Apache License 2.0.
Physics-IQ [26]:
DVSC2023 (Natural Video Source for Classifier Training): As explained in Section 5 of 50,000 natural videos for training (Section 6 was sourced from DVSC2023 47 DVSC2023 is under the CC BY-SA 4.0 llicense.
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Reviews:
[{"review_id": "Ft86XFXoud", "reviewer": "Reviewer_LGaM", "summary": "This 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.", "questions": "- 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?", "limitations": "This 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?", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 4, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "My score will be maintained based on my initial reviews and the quality of the author's rebuttal.", "rebuttal": "First of all, thank you very much for highlighting and appreciating the simplicity and effectiveness of our method. \n\n*“The paper can benefit greatly from a robustness analysis.”:* We agree that a robustness analysis would greatly benefit the paper.\n\n*(1) Choice of vision encoder.* We would like to draw attention to Figure 2b where we systematically tested eight encoders (CNNs, Transformers, Contrastive) and selected DINOv2 due to its ability to separate natural and AI video curvature.\n\n*(2\\) Time interval between frames* and *(3) duration of the video.* Please refer to the \"Supplementary Material: zip\" folder on the submission page where you will find the impact of *video length* and *sampling density/interval between frames* on performance and inference time (Figure 1\\) and the impact of *temporal window position* (Figure 2). \n\n*(4)* ReStraV’s is intentionally artifact-agnostic. Instead of targeting specific artifacts (e.g., GAN-specific frequency patterns or face-swapping irregularities) it quantifies a more fundamental deviation from natural world dynamics. We hypothesize that models struggle to replicate natural video's smooth temporal structure, leading to higher curvature. This is validated by ReStraV's strong performance across diverse AI generators (Tables 2-4) and its success on the Physics-IQ test.\n\n*(5) Feature Ablation:* Following your suggestion we performed a permutation importance analysis, training our MLP classifier on 50,000 AI-generated samples from VideoProM and 50,000 natural videos from DVSC2023 (Section 6):\n\n| Feature         | Mean F1-score drop (%) | Standard Deviation (%) |\n|-----------------|------------------------|------------------------|\n| curvature\\_mean | 8.56%                  | 0.31%                  |\n| curvature\\_var  | 3.11%                  | 0.17%                  |\n| distance\\_min   | 1.53%                  | 0.09%                  |\n| distance\\_max   | 1.31%                  | 0.11%                  |\n| curvature\\_max  | 0.65%                  | 0.05%                  |\n| distance\\_0     | 0.61%                  | 0.07%                  |\n| distance\\_min   | 0.51%                  | 0.07%                  |\n| distance\\_mean  | 0.09%                  | 0.04%                  |\n| distance\\_var   | 0.11%                  | 0.13%                  |\n| curvature\\_min  | 0.06%                  | 0.04%                  |\n| distance\\_5     | 0.09%                  | 0.02%                  |\n| curvature\\_1    | 0.08%                  | 0.03%                  |\n| distance\\_2     | 0.07%                  | 0.04%                  |\n| curvature\\_2    | 0.07%                  | 0.02%                  |\n| distance\\_4     | 0.06%                  | 0.01%                  |\n| curvature\\_0    | 0.05%                  | 0.05%                  |\n| curvature\\_5    | 0.04%                  | 0.03%                  |\n| curvature\\_6    | 0.03%                  | 0.05%                  |\n| curvature\\_3    | 0.03%                  | 0.03%                  |\n| curvature\\_4    | 0.02%                  | 0.01%                  |\n| distance\\_1     | 0.02%                  | 0.02%                  |\n| distance\\_3     | 0.15%                  | 0.03%                  |\n| distance\\_6     | 0.19%                  | 0.04%                  |\n\nMean curvature is the most critical feature, followed by curvature variance. This validates Figure 5, where features with the clearest separation between natural and AI video distributions proved most important.\n\n*“Natural videos often have shot transitions.”:* Your point about shot transitions is well-taken. There is, indeed, no reason to expect the straightening hypothesis to hold across shots. Presumably, shot transitions will come with a high curvature. This affects both shot transitions in natural and AI generated videos. If the latter have, on average, more shot transitions, then this might explain part of the performance of our classifier. Using \"scenedetect\"  on 13,000 AI-generated and 13,000 natural videos, AI videos averaged 5.89 seconds shot duration, while natural videos averaged 7.04 seconds:\n| Model        | Average Duration (seconds) | Average # of scene cuts in first 2s |\n|--------------|----------------------------|-------------------------------------|\n| PIKA         | 2.97s                      | 0.00                                |\n| Show1        | 3.62s                      | 0.00                                |\n| ModelScope   | 3.84s                      | 0.00                                |\n| Gen2         | 4.00s                      | 0.00                                |\n| HotShot      | 4.60s                      | 0.01                                |\n| Crafter      | 5.43s                      | 0.00                                |\n| MoonValley   | 5.59s                      | 0.00                                |\n| T2vz         | 6.01s                      | 0.00                                |\n| Vc2          | 6.98s                      | 0.10                                |\n| **Natural** | **7.04s** | **0.08** |\n| MorphStudio  | 7.05s                      | 0.02                                |\n| OpenSora     | 8.22s                      | 0.04                                |\n| Sora         | 12.40s                     | 0.04                                |\n\nWe use a relatively short, 2s window, which reduces the probability of encountering a hard scene cut. Our ablation study (Supplementary Material, Figure 2) confirms detection performance is robust regardless of window position, indicating insensitivity to local video dynamics like shot transitions. This is further supported by promising results on models generating longer scenes (Sora, OpenSora, MorphStudio). We will add this important discussion to the paper.\n\n*“It would be nice to show a few examples of real videos and their synthetic replicas”:* Thanks for suggesting example frames of real and synthetic videos for the appendix. In Section A.10.3 “Qualitative Examples of Plants Task” we directly compare frames from natural videos (HD-VG/130M) with their synthetic counterparts from generators like Mora, MuseV, SVD, and CogVideo. In addition, we will add randomly sampled frames across tasks to add more diverse examples.\n\n**Miscellaneous Questions:**\n\n* *“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?”:* Thank you for the insightful points about adversarial models. *S*traightness is task-dependent and not universally desirable. For example, a video prediction model must match the input video's curvature rather than minimize it as showed in Harrington et al.'s 'Exploring Perceptual Straightness in Learned Visual Representations' (ICLR 2023). We will add this discussion to the paper to highlight that our goal is not pure straightening but relative straightening (of natural vs. synthetic videos) which enables discriminating the two.\n\n* *“Why include the CLS token?”:* The CLS token summarizes the entire frame, while patch embeddings capture local details, enabling the model to identify both large-scale and fine-grained inconsistencies. We will clarify this in the text.  \n* “In section 6, how was the “seven distance values” chosen? Why 7?”: We considered 24 frames with a sampling interval of 3 (8 final frames in total) from a fixed 2-second window, *selected as the shared duration across all datasets and generators to avoid bias and missing data* (see Section 4\\). This choice also optimizes detection accuracy and computational efficiency, as shown in our ablation studies (Supplementary Material). We will further justify this in the paper.  \n\n*“I worry that pairwise distance and curvature is just a temporary “bias” of those video generative models rather than an intrinsic property.”*  Great questions. We have three pieces of evidence that suggest the geometric signal used by ReStraV is not just a temporary bias but, somewhat, robust to future models:\n\n* *Performance on a New SOTA Model Veo3 (Out-of-distribution):* In response to your concern, we tested our method on videos from the new Veo3 model.  This model is considered to not typically suffer from poor object persistence. A simple Linear Regression trained with the model and futures as in Section 5 when tested on 100 Veo3 and 100 natural videos achieve: ACC \\= 0.832 | F1 \\= 0.851 | AUROC \\= 0.869. We expect performance to improve with a larger training set including Veo3 videos.  \n* *Generalization to Future Models:* We tested this exact scenario. As shown in Table 3 (and in the previous point with VEO3), ReStraV was trained on older generators and then tested on videos from Sora, a more advanced \"future generator\". The method achieved 92.85% AUROC. While not perfect, those results suggest a degree of future-proofing.  \n* *Effectiveness on Low-Motion Content:* We tested our method on the GenVidBench \"Plants\" task, its most challenging subset of GenVidBench. And we argue that it is since it often features subtle movements like wind and complex, stochastic patterns rather than dynamic action. As shown in Table 4(b), ReStraV achieved an ACC. of over 93%. This could indicate ReStraV detects unnatural patterns even in the presence of slow or little motion. While no detection method is guaranteed to be permanent, these results suggest that the geometric trajectory analysis of ReStraV captures a more fundamental property of AI-generated videos."}, {"review_id": "9e192wBYGI", "reviewer": "Reviewer_98v6", "summary": "The 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.", "questions": "- 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.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "** Strengths**\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.", "was_revised": true, "final_justification": "The detailed responses and the clarifications have addressed the concerns and questions. Raising my score.", "rebuttal": "We sincerely thank the reviewer for the insightful review. The feedback helped us identify areas where we can strengthen our manuscript. We are glad the reviewer acknowledges that our method is \"lightweight\" and \"delivers good performance\".We have worked to address all raised points below.\n\n*“The premise of perceptual straightening in SSL models is not justified”***:** This may be a misunderstanding that we will also clarify in the final version of the paper. We do not *“assume that the latent space of an off-the-shelf SSL model inherently straightens representations of natural videos”*, but we test which one reveals the AI-generated ones instead. We systematically investigate whether this is the case empirically: 1\\) We show in Figure 2B that among eight diverse encoders, DINOv2 is most effective at maximizing the curvature difference between real and AI-generated content. 2\\) We demonstrate that simple geometric and statistical features of the curvature and distance trajectory, as shown in Figure 5, are highly discriminative. 3\\) We show that these features, when fed into simple classifiers, create a powerful detector that achieves SOTA performance, outperforming complex, dedicated detectors and foundational models (Gemini 1.5) on challenging benchmarks \\[1,2,3\\] (Tables 3 & 4 and Figure 7).\n\n*“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”***:** We see your point about clarity. Figure 4 (t-SNE) is meant to illustrate the emergent *class separability* in DINOv2's space together with Figure 1A, not absolute straightness which is very hard to visualize in the high dimensional embedding space. Figure 8 shows the raw curvature signal *before* we extract the statistical features that lead to our SOTA classification results as qualitative examples–one can see the separation in curvature between natural and AI in most plots. Following your comments, we will draw a convex hull or a density contour around the Natural vs. AI clusters to provide a clear visual boundary and emphasize the separation our method leverages in Figure 4 (t-SNE).  We will improve clarity in the caption of both figures. \n\n*“The choice of the SSL model is rather adhoc”***:** We agree with you that a purely theoretical motivation for selecting a specific encoder is not yet established. Our approach was therefore a principled empirical one. As shown in Figure 2B, we systematically screened different encoders from diverse architectures , e.g., CNNs (AlexNet, VGG-16, ResNet-50, SIN-ResNet-50), Transformers (CLIP,DINOv2), and training objectives, e.g., SSL (SimCLR-R50, BYOL-R50). We selected DINOv2 because it empirically demonstrated the largest discriminative power. In the absence of an *a priori* theory, this systematic empirical procedure appeared to be the most principled approach that avoids any unjustified or *ad hoc* choices. *We are very interested in any theories, suggestions or references that could explain this outcome, and we would be eager to incorporate them.*\n\n*“The paper does not cite relevant works from the perceptual quality assessment literature”*: Thank you for highlighting the relevant literature on VQA. This is a great suggestion\\! The main difference is that the cited VQA papers address the problem of scoring perceptual quality, while ours focuses on AI-generated video detection. *Given the high perceptual quality of the latest AI-generated videos, these two tasks may be quite distinct.* We will gladly cite and discuss these related works in the revised manuscript.\n\n*“the comparison is missing relevant works such as TALL, NPR, STIL, DeMamba etc”*: We referred to the SOTA baselines in recent benchmark papers \\[1,2,3\\] for general AI-generated video detection. We included eight image based detectors (Table 3\\) and nine general video detectors (Table 4\\) and a foundational model (Gemini 1.5, Figure 7). Thank you for suggesting additional baselines. We agree that it would improve our paper to extend our test with the applicable ones. TALL, NPR and STIL are highly specialized for *deepfake face detection*, and thus cannot be used on the more general datasets we analyze. DeMamba \\[4\\] is a general detector; however, the model weights are not accessible at this point according to the author’s github (see issue \\#16: “Due to the company's open source policy, we are temporarily unable to provide open source weights”). Nevertheless, the authors provide the full dataset to the public. We therefore reproduced the challenging “one-to-many generalization task” experiment of Table 4 in DeMamba \\[4\\]. The one-to-many generalization task involves training on one baseline category and then testing on each subset. The paper will be updated with the following additional results:\n\n**One-to-Many Generalization Task (avg. performance);** Test on Sora, MorpStudio, Gen2, HotShot, Lavie, Show-, MoonValley, Crafter, ModelScope and WildScrape:\n\n**Training Subset: Pika**\n\n\n| Model              | R        | F1       | AP       |\n|--------------------|----------|----------|----------|\n| NPR                | 0.5144   | 0.5306   | 0.6497   |\n| STIL               | 0.7383   | 0.5165   | 0.6304   |\n| MINTIME-CLIP-B     | 0.2166   | 0.3263   | 0.5738   |\n| FTCN-CLIP-B        | 0.6287   | 0.6423   | 0.7194   |\n| TALL               | 0.7141   | 0.5572   | 0.6225   |\n| XCLIP-B-FT         | 0.6096   | 0.6579   | 0.7831   |\n| DeMamba-XCLIP-FT   | **0.7572** | 0.7263   | **0.8166** |\n| Ours (RestraV)     | 0.7354   | **0.8272** | 0.7967   |\n\n  \t\t\t\t\n\n**Training Subset: SEINE**\n\n| Model              | R        | F1       | AP       |\n|--------------------|----------|----------|----------|\n| NPR                | 0.4618   | 0.5385   | 0.6111   |\n| STIL               | 0.7239   | 0.5057   | 0.6083   |\n| MINTIME-CLIP-B     | 0.6135   | 0.6538   | 0.7443   |\n| FTCN-CLIP-B        | 0.6279   | 0.6991   | 0.8036   |\n| TALL               | 0.6568   | 0.6085   | 0.6805   |\n| XCLIP-B-FT         | 0.7201   | 0.7898   | 0.8880 |\n| DeMamba-XCLIP-FT   | 0.8098   | 0.7873   | **0.8943** |\n| Ours (RestraV)     | **0.8199** | **0.8981** | 0.8543   |\n\n**Training Subset: OpenSora**\n\n| Model              | R        | F1       | AP       |\n|--------------------|----------|----------|----------|\n| NPR                | 0.5929   | 0.5232   | 0.5763   |\n| STIL               | 0.4336   | 0.4892   | 0.5256   |\n| MINTIME-CLIP-B     | 0.6889   | 0.7269   | **0.8286** |\n| FTCN-CLIP-B        | 0.1863   | 0.3005   | 0.5689   |\n| TALL               | 0.4916   | 0.5315   | 0.5710   |\n| XCLIP-B-FT         | 0.6615   | 0.6497   | 0.7154   |\n| DeMamba-XCLIP-FT   | 0.7382   | 0.6713   | 0.7382   |\n| Ours (RestraV)     | **0.7707** | **0.7965** | 0.7167   |\n\n*“Discussion on why the proposed method works is limited”:* We’re happy to expand our current discussion of hypotheses (ll. 295-302, 312-318) in the manuscript within the given page limit. One possible explanation is that SSL models like DINOv2 might develop a strong inductive bias for subtle statistical regularities of the natural world. While video generators like Sora can produce photorealistic videos, they still struggle to perfectly replicate these complex physical dynamics. Even SOTA generators can exhibit temporal inconsistencies or generate implausible physical scenarios. These failures possibly violate the learned statistical regularities of the DINOv2 encoder. In essence, ReStraV works by using an SSL model trained on natural image statistics as an \"expert observer\" to spot the subtle, unnatural geometric fingerprints left by the imperfect physical simulations of even the most advanced AI video generators.\n\n\\[1\\] Wang et al. Vidprom: A million-scale real prompt-gallery dataset for text-to-video diffusion models, Neurips, 2024  \n\\[2\\] Ni et al. Genvidbench: A challenging benchmark for detecting ai-generated video, 2025  \n\\[3\\] Motamed et al. Do generative video models understand physical principles?, 2025  \n\\[4\\] Chen et al, DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark, 2024"}, {"review_id": "zRCEYjaOID", "reviewer": "Reviewer_YPpG", "summary": "This 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.", "questions": "See weaknesses", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "On the positive side, the paper gives a novel approach and obtains strong empirical results. On the negative side, the method lacks certain theoretical principled guarantee. I will retain my original positive score.", "rebuttal": "Thank you for your positive feedback on our paper's clarity (*“clearly written”*), strong empirical results (*“significant improvements”*), and motivating theory (*“well-motivated by perceptual neuroscience literature”*). We appreciate the opportunity to address your questions:\n\n*“The experiments only evaluate one commercial video generation model (Pika)”*:  We would like to clarify a potential misunderstanding regarding the benchmarking that we will also clarify in the paper. *We cover a total of 18 generative models across multiple datasets, beyond Pika*. In Table 2 we evaluated four generators (*Pika, VideoCrafter2, Text2Video-Zero, ModelScope*) and tested generalization to the advanced *Sora* model as a \"future generator\" in Table 3\\. We benchmarked against four different modern generators (*MuseV, SVD, CogVideo, Mora*) in Table 4\\. We used matched real/generated pairs from *Runway, Pika, and Sora* in Physics-IQ Dataset (Figure 7). Additionally, during the rebuttal phase we tested our method on videos from the new Veo3 model. And we have reproduced the One-to-Many Generalization Task from Table 4 of \\[1\\], testing on Sora, MorpStudio, Gen2, HotShot, Lavie, Show-, MoonValley, Crafter, ModelScope.\n\n*“The core insight of the paper (e.g., the left and middle parts of Figure 1\\) appears closely derived from prior work \\[1, 2\\]”:* Thank you for the point about the novelty of the work. We are indeed deeply inspired by the foundational work on perceptual straightening. Our novel contribution is not the concept itself, but its successful repurposing and practical application to the distinct and modern problem of AI-generated video detection, where we obtain SOTA results. To the best of our knowledge, we are the first to apply ideas from the perceptual straightening hypothesis to this task, and we will revise our manuscript to make this clear\n\n*“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”:* We agree that this is a very important point that equally applies to all published detection methods. Similar to the “arms race” between adversarial attacks and defenses, the field of AI-generated video detection might see a similar dynamic. We explicitly acknowledge this in our paper's Discussion section under \"Limitations\". In the field of adversarial attacks, publishing attacks and defenses ultimately led to the development of robust techniques like adversarial training; we are hopeful that publishing AI-generated video detectors will similarly raise the bar to inspire a future generation of AI video generation models with more physical realism.\n\n\\[1\\] Chen et al, DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark, 2024"}, {"review_id": "04BZnxwKFF", "reviewer": "Reviewer_NU9X", "summary": "Grounded 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.", "questions": "1. 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?", "limitations": "Yes, 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.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \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?", "was_revised": true, "final_justification": "I appreciate the authors' original motivation to distinguish between trajectory patterns in natural and AI-generated videos, as well as their efforts to clarify the details and additional comparisons suggested by other reviewers. I am happy to keep my orginal positive score.", "rebuttal": "Thank you very much for the positive evaluation of our work *(“simplicity and effectiveness”, “remarkable results”, “fresh and valuable perspective*”) as well as the feedback and clarifying questions.\n\n*“In 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?”:* Thank you for catching the discrepancy in the appendix. We will fix line 622 to make it clear that Figure 10 is about LR and MLP in the final version of the paper.\n\n*“Compared to RF, what are the advantages of MLP? Is the computational time cost of RF measured on the CPU?”*: Regarding the comparison between RF and MLP, as detailed in Table 1, while RF has a marginally higher accuracy (94.24% vs. 94.17%) and F1-score (96.53% vs. 96.48%), our simple two-layer MLP (64→32  with a native implementation in sklearn) achieves a higher AUROC (98.63% vs. 98.03%). This indicates the MLP provides a better trade-off between true positives and false positives across different thresholds, making it a more robust classifier overall. The inference time for the entire pipeline (DINOv2 feature extraction on a GPU \\+ classifier on CPU) is nearly identical for both (RF: 48.14 ms, MLP: 48.12 ms). The classifier portion itself is a minimal component of this total time.  \n \n*“If AI-generated videos are augmented with simulated noise or compression artifacts, would the algorithm still be able to distinguish them effectively?”:* This is an excellent question regarding the method's robustness. Our core hypothesis is that AI-generated videos exhibit more curved, irregular trajectories in the DINOv2 representation space compared to the straighter paths of natural videos. Adding uncorrelated noise or compression artifacts would introduce high-frequency, erratic changes between frames. In the representation space, this would almost certainly make a video's trajectory even more curved. Therefore, augmenting an AI-generated video with such noise would likely increase its detected curvature, making it more distinct from a natural video and thus easier for our method to classify.\n\n*“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?”:* Thank you for the question, this is a key aspect of our method's efficiency. Please refer to our ablation study in \"Supplementary Material: zip\" Figure 1 and 2\\. For a fixed 2-second video clip, the best performance is achieved with \\~20-24 sampled frames. Using significantly fewer frames fails to capture enough temporal information, while using too many (e.g., 60 frames from the same 2s clip) actually degrades performance, likely by introducing redundant, high-frequency signals. Our choice of 24 frames with a sampling interval of 3 (8 final frames in total) is therefore a well-justified trade-off between accuracy and efficiency. When analyzing longer videos, performance generally improves. We observed that accuracy and AUROC increase as the source video length goes from 1 to 5 seconds, though with diminishing returns after the 2-3 second mark."}]

Author rebuttal:

Meta-review: The paper proposes ReStrav for detecting AI-generated videos. The contribution exploits a perceptual straightening hypothesis - that real videos tend to follow straighter trajectories in a DINOv2 representation space than synthetic ones.  Some interesting results are presented including on unseens models (Veo3) indicating this may be a generally useful detection feature non-specific to learned data-specific priors.

The paper has received unanimous recommendation to accept in the final, post-rebuttal discussion stage (2x firm accept, 2x borderline accept).

NU9X is generally positive and considers the insight valuable.

98v6 was initially borderline and had doubts re: embedding model choice but was convinced by the rebuttal experiments and raised their rating.

LGam and YPpH are both positive but wanted to see more theory vs. empirical observation of the phenomenon.  This was not really provided in the rebuttal but some diversity of model choice was explored and greater explanation / conclusion offered.

Overall the AC considers this an interesting and insightful paper on AI video detection, and agrees with the unanimous decision of the reviewers to accept the paper.
Decision: Accept (poster)