Seeing What Matters: Generalizable AI-generated Video Detection with Forensic-Oriented Augmentation
Authors: Riccardo Corvi
Davide Cozzolino
Ekta Prashnani
Shalini De Mello
Koki Nagano
Luisa Verdoliva
Venue: NeurIPS 2025 poster ()
Keywords: generative AI
media forensics
synthetic video detection
Primary area: applications

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

Seeing What Matters: Generalizable Al-generated Video Detection with Forensic-Oriented Augmentation
Riccardo Corvil.2; Davide Cozzolino', Ekta Prashnani', Shalini De Mello?, Koki Nagano", Luisa Verdoliva'
University Federico II of Naples, 2NVIDIA
Abstract
Synthetic video generation is progressing very rapidly. The latest models can produce very realistic high-resolution videos that virtually indistinguishable from real ones. Although several video forensic detectors have been recently proposed, they often exhibit poor generalization, which limits their applicability in a real-world scenario. Our key insight to overcome this issue is to guide the detector towards seeing what really matters. In fact, well-designed forensic classifier should focus on identifying intrinsic low-level artifacts introduced by generative architecture rather than relying on high-level semantic flaws that characterize specific model. In this work, first, we study different generative architectures, searching and identifying discriminative features that are unbiased, robust to impairments, and shared across models. Then, we introduce a novel forensic-oriented data augmentation strategy based on the wavelet decomposition and replace specific requenccy-elltter bands to drive the model to exploit more relevant forensic cues. Our novel training paradigm improves the generalizability of Al-generated video detectors, without the need for complex algorithms and large datasets that include multiple synthetic generators. To evaluate our approach, we train the detector using data from a single generative model and test it against videos produced by a wide range of other models. Despite its simplicity, our method achieves significant accuracy improvement over state-of-the- art detectors and obtains excellent results even on very recent generative models, such as NOVA and FLUX.
Introduction
In recent years, the field of Al-based video generation has witnessed rapid advancements. Several flexible tools exist, based on diffusion models, that generate high-quality videos from general conditional inputs like text and images [41, 77, 95]. These powerful tools enable professionals use AI for innovative and creative applications, such design, marketing, and entertainment. However, the misuse of such technologies also raises ethical and social concerns related to the spread of disinformation, the violation of intellectual property rights and more generally the erosion of trust in digital media [28, 5, 46]. Therefore, there is an urgent need to develop effective methods for distinguishing real from Al -l-generat videos.
Previous work in video forensics focused mostly on facial forgery detection [66, 83, 85, 33] and proposed solutions that are specifically tailored to faces, for example, by analyzing head pose facial landmarks and appearance [54, 15], inconsistencies in skin texture or lip motion [49, 94, 34],
Part of the work was done during an intersship NVIDIA. Project page: https::/ / htt///gittti gigttuu..bb... com/ com/grip wwrggrrp-uming haappauniaa//aammeeepeeaeeaaiaaa www.uuiainaaaeeeeeeeeee hwtpppps--yyyyeeeiiieeeeeeetttte
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Fake
Augmented
1/18
1/8
W
W-
1/4
Thumsker
Transform
1/2
Figure I: Synthetic video generators leave distinct traces, that are observed the frequency spectrum. We leverage this observation to enhance their generalizability. To this end, we propose a novel training time data augmentation strategy based on wavelet-bands that forces the model to learn the frequency components that best distinguish real from synthetic content. Fakes are also generated through video autoencoding to avoid semantic hias and trick the model into exploiting low-level forensic traces left by the modern video generation architectures. Our training paradigm improves the generalizability the detector without the need for complex algorithms and large datasets that include multiple generators.
unnatural heart rate variations or motion [17, 24] or identity-based biometrics [l. 61]. However, these approaches are inherently limited to facial content and struggle to generalize to more complex and semantically rich synthetic videos generated by modern diffusion models. Only a few works
have been designed so far for fully generated video detection [12, 48, 52, 3, 43]. Such methods focus primarily on designing novel and often complex architectures, neglecting the critical role of the data. In fact, in forensic applications, the choice of training and test data is essential to ensure that detectors learn generation-relate cues, rather than spurious correlations encoded in the data [74, 50, 10j. Even though the presence of dataset biases is well-known risk in machine learning, it still forms a significant and underestimated problem in forensics research. In [9], it was shown that popular benchmark dataset included real and manipulated images compressed at different JPEG quality levels, causing classifiers to learn compression artifacts instead of tampering cues, leading to poor generalization. This same issue was recently observed for the detection of Al-generated images [31], while other works have pointed out the presence of other possible biases for the same task, such as content resolution bias [10, 65, 32].
In this work, our aim is to design a synthetic video detector that truly based on generation-specific artifacts, i.e. traces related to the generation process. This is because methods that rely exclusively on data-driven learning or semantic errors in generation tend to overfit to the training data or to the artifacts introduced by specific generators, e.g., certain semantic visual cues, such as the lack of perspective or temporal consistency in the generated videos, which lead to poor generalization. We overcome this limitation by identifying and integrating priors into the synthetic video detectors that leverage forensic traces that are consistently present across diverse generative models.
To this end, we address the following questions: i) What are good discriminative and robust forensic traces present in Al-generated videos? ii) What is good strategy to exploit them? As a first step, we identify the hidden forensic traces that are shared by modern video Al-generators and arise from the generative architectures themselves. Prior art has shown that the up-sampling operations inherent in the synthesis network give rise to quasi-periodic patterns that are clearly visible as peaks in the high-frequency portion of the Fourier spectrum [93, 69]. As a consequence, several works exploited fighffrequency traces for Al-generated image detection [30, 47, 10]. Unfortunately, fighffreuuency components are severely degraded by compression, especially by the strong compression usually adopted for videos, with the effect of washing out the most prominent forensic traces (see Fig.3). However, Diffusion- siffuusion-basedd generated content differs significantly from natural content not only at high but also at medium frequencies [18]. This latter finding is especially important in this context since we show that video compression impacts less the video's 's diagonal mid-high frequencies, which therefore out to be both discriminative and robust, good basis build effective detectors.
To exploit these traces we chose not to work on new detection architectures but, inspired by the recent forensics literature [32], to focus on the crucial training phase, in order to emphasize the most

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meaningful artifacts and thus guide the detector in learning the right features. To this end, we build a videos with the same caption and carry out two forms of augmentation
dataset of paired (see Figure. 1): 1) we add controlled fake videos by injecting the mrchitctture-reate real videos through video autoencoder. This has already been shown to be effective in avoiding possible semantic biases [65, 321; 2) we push the model to exploit middle frequencies by a tailored augmentation strategy that works on selected bands of a multiscale wavelet decomposition of the video. This has the advantage of avoiding looking at specific compression cues that affect both real and generated videos.
artifacts on
Overall, we make the following contributions:
we find that inconsistencies in the middle-high (diagonal) frequency content of synthetic video are discriminative, robust, and common across several different video generators, even more recent models;
we propose a novel augmentation strategy that works on the wavelet bands to guide the model towards exploiting such cues;
we show that by including this simple strategy we can outperform current SoTA methods in terms of generalization. Across 15 different generators, our approach yields an average improvement of around 12% in terms of accuracy.
2
Related Work
Text-driven video generation. Synthetic video generation has advanced rapidly in recent years, driven largely by powerful tools like diffusion-based models. Early methods like Vixe27ideo- [41], Modelscope [77], and Hotshot-XL [55] adapted image generators for video synthesis, but struggled with challenges such as temporal coherence and motion consistency. Models Models-including
Mochi-1 [73], Allegro [95], Opensora-Plan [45], and Cog VideoX [86]-introduce dedicated ar- chitectures leveraging 3D causal autoencoders and 3D transformers, enabling better modeling of spatio-temporal relationships. These models also achieve stronger semantic alignment with input text and produce videos with higher quality and improved temporal consistency [36]. Recently, autoregressive models, such as Loong [8 and NOVA [25], have shown impressive performance for video generation. Although they are based on a completely different generation paradigm, we show that they still exhibit anomalies similar to diffusion models that can be exploited by the forensic classifier.
Detection of Al-generated videos. Initial approaches for detecting fully generated videos focused on human motion cues [6], while others adapted image-based forensic detectors using few-shot learning [75]. More recent methods exploit both spatial and temporal artifacts. Bai et al.[3] propose two-branch CNN to capture spatial anomalies and optical flow inconsistencies, while Chang et a use three 3D CNNs that target appearance, motion, and geometry. Liu et al. [48] take different path, feeding RGB frames and reconstruction errors into CNN+LSTM model, based on the idea that diffusion models can better reconstruct synthetic images than real ones. a principle previously applied to images [82]. Other methods leverage vision-language foundation models like CLIP [52], X-CLIP [12], and LL [71] to capture both spatial and temporal inconsistencies.
more recent approach [43] also introduces a loss function to encourage attention over diverse spatial regions, improving detection beyond facial areas. Differently from such works that focus on the architectural design and train on datasets with standard augmentation, we propose new training paradigm to capture more robust levelevel features, which ensure better generalization across different generative models.
Frequency artifacts. AT-generated images often exhibit distinctive Fourier-domain signatures that reveal their synthetic origin. This was shown already for GAN generated images in [93, 69], where the detector was developed to exploit the spectral peaks introduced by the upsampling operations common to such architectures. Building on frequency-domain analysis, prior work [30, 27] trains models on Fourier spectra extracted from real and synthetic images, while [26] shows that L-ANggeneraee images deviate from natural spectral distributions and proposes a simple detector based on the energy spectrum. In [39], a spectral- based adversarial training is proposed to encourage the GAN generator to better reproduce natural spectral distributions, while [35] shows that this training is not sufficient to make -egerrerd images undetectable. Also Diffusion models poorly reconstruct mid-band frequencies compared to real images [18]. This is exploited in [16] that leverages frequency-guides reconstruction to identify the information that the model struggles to reconstruct. Other works force
3

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Real data
Pyramid Flow
Mochi-1
Allegro
NOVA
Figure From left right: a real video from the dataset proposed in [14] and synthetic videos generated using the same associated prompt "A scuba diver in the ocean surrounded by fishes" with Pyramid Flow, Mochi- 1, Allegro and NOVA. For each of them we show its spatial power spectrum Sya (u, v) (bottom-right) and its temporal-see -patial power spectra (wn e) and (y, w) (top-right and bottom-left).
the network focus on ligh-frequencier related features [62, 72] or exploit relationships of spatial and frequency domains [80]. However, none of these works accounts for the impact of compression in their method design.
Augmentation strategies. Data augmentation is essential in computer vision, improving both generalization and robustness by increasing data diversity. It also plays a fundamental role in the detection of synthetic content. Wang et al. [79] showed that augmentation is key to generalizing across unseen generative models, with operations like blurring and JPEG compression being especially effective. Despite its impact, augmentation has received limited attention in the forensic literature. Most existing strategies are borrowed from computer vision and rely high-level transformations such as brightness and contrast adjustments [7, 19], or cut-out and mix-up techniques [78]. Additional high-level strategies have been proposed for facial manipulations in videos [44, 233. A different path is pursued in [84] where overfitting to method-specift cues is addressed by proposing latent space augmentation by simulating variations within and across forgery features. Instead, [40] is introduced Mixup augmentation, which dynamically diversifies frequency characteristics of training samples to mitigate spurious shortcuts and improve generalization. However, we believe that in order to enhance video forensic traces, it is important to design an augmentation strategy that drives the model to focus on the most relevant cues, as it is done for other low-level vision applications [13, 87, 70].
3 Proposed Method
3.1 Video forensic artifacts
A key step in developing effective detectors for synthetic videos is to identify the most discriminative features. Generators can introduce visual and semantic artifacts, such as geometric distortions, layout inconsistencies, color mismatches, or temporal incoherence. However, these visible errors tend to reduce generative technologies continue to evolve and it is likely that they may soon disappear altogether. To ensure long-term robustness, our approach focuses on low-level artifacts intrinsic to the generative architecture. In this section we analyze such subtle traces.
Fingerprints in the Fourier domain. Early research on synthetic images has shown that, much like real cameras, which imprint each photo with unique device/camera specific signature [51, 21]. synthetic architectures also leave distinct fingerprint in every generated image [53, 89]. Researchers used these artificial fingerprints to develop methods capable of identifying synthetic images and even trace them back to the specific architecture used for their generation [2]. For synthetic images, these artifacts are clearly visible in the frequency domain [69, 18] and the same holds for frames extracted from synthetic videos [75]. 22g.2, we show the average power spectra of the residual videos obtained by removing the high-level semantic content of the original videos through denoiser. More specifically, we compute the residual of the i-th video as
r (m, n, (m n, p)
(I)
where n. (m, n. p) is the p-th frame of the video with size M N x and D denotes the CNN-based denoiser proposed in [92], which is applied to each frame individually
P, m and n are spatial coordinates

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Synthetic video before compression
Synthetic video after compression
Compressed real video
Generation spatial peaks
Generation temporal peaks
Compression peaks
Figure 3: Top: Spatial and temporal-spatil power spectra of OpenSora-Plan videos before and after compression, compared to those computed from real (compressed) video. Bottom: close up the power spectra presented on the top. Fourier-domalt originated by compression are highlighted by red boxes. We can notice that after compression most of the peaks reduced and compression traces (peaks concentrated along the horizontal and vertical directions) visible synthetic videos similar real ones.
peaks due to video synthesis (forensic artifacts) are highlighted by circles. Peaks
with the noise parameter a set to 1. Then, we evaluate the 3D Fourier transform:
M.N.P
Ra(u, v. w)
Σ
ri(m, n)e 52z( m-+ not Wr)
m.R.p=1
and compute the spatial power spectrum by averaging the spectra of all frames of I 150 videos:
Sya (u,
Σ
[R(u, v, w))
The spatial power spectrum accounts for the fraction of the total image power concentrated given (vertical, horizontal) frequency pair ( ( ). On the other hand, by averaging the power spectra of all videos along the rows obtain the cemporal-spatial power spectrum,
Sw u)
Σ
Σ Ra(u,,,w)) M
which accounts for the fraction of the total image power concentrated at given (temporal, horizontal) frequency pair (%; )). Similarly, we can compute the temporal-spatia power spectrum Sye (wi, v) concentrated given (vertical, temporal) frequency pair. The spatial power spectra of Fig.2 exhibit typical spectral peaks (visible as bright spots) caused by the upsampling process in the generative architecture. Interestingly, similar peaks also visible along the temporal direction. Notice, however, that these peaks are not present in the real video (Fig.2, first column).
Impact of video compression. In realistic scenarios requiring efficient storage and transmission, synthetically generated videos usually compressed using standard codecs. These codecs exploit redundancies along both spatial and temporal dimensions, but to achieve higher compression, they also tend to smooth the original signal, thus removing most of the high-frequency components, and valuable forensic artifacts along with them. Indeed, seeing Fig.3, we can note that, after compression, most peaks vanish from the spatial spectra (left) and all of them from the temporal spectra (right). In addition, we can observe that compression, due to its block-wist wise processing, also introduces some strong peaks of its own, both in synthetic and real videos. This suggests that looking vertical and horizontal directions could easily trick detector into wrong decisions. Instead we can observe that the peak along the diagonal direction is still present, and can provide more meaningful and robust cue. Notably all synthetic generators exhibit such diagonal peaks, even autoregressive models (Fig. 2), which survive even after compression (see appendix). This observation motivates our focus on these artifacts, that are both shared across different generators and robust compression.

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3.2 Training strategy
The analysis described above has highlighted the need to focus on artifacts in the mid-high frequency range along the diagonal directions. To this end, it is worth noting that for enffusiionbbased generated images diagonal frequencies were found to be more difficult to synthesize than vertical and horizontal ones in [18], which further supports our conjecture. To implement our training strategy, we include two forms of augmentation: the addition of simulated fake videos in the training dataset by fingerprints injection and the inclusion of an augmentation strategy during training based on the wavelet transform.
Artificial injection of forensic eues. To encourage the detector to look at low-level artifacts, we add fake videos in the training dataset that are visually indistinguishable from real ones, but embed the traces related to the generation architecture. To achieve this, real videos are reconstructed with the same autoencoder used during generation. This idea is not new, and is actually gaining ground lately. Several works have simulated forensic artifacts train the detectors both for GAN-based images [93, 37] and for diffusion models [65, 32]. Building fakes in this way also removes the semantic content bias and encourages the detector to identify intrinsic artifacts and patterns related to the generation process versus relying on differences in the semantic content. We adopt the Variational Autoencoder ( (VAE) used by Pyramid Flow [38]. This is a 3D convolutional neural network trained on the WebVid-1OM dataset [4] with a latent space that compresses videos both spatially and temporally using a downsampling ratio of 8 x x 8. Its structure is similar to MAGVIT-v2 [88], incorporating causal convolution in time and pixel shuffle operations for decoder upsampling.
Augmentation by wavelet-band replacement. We propose a new form of augmentation that based on the wavelet decomposition. During training, for fake videos we replace the low- frequency bands (plus those along the horizon- tal and vertical directions) with those of the real counterpart. In this way, the detector is forced to base the decision on the mid-high frequency di- agonal components. The use of wavelet decom- position helps to easily conduct such replace- ment, since this transform splits the signal into multiple frequency bands preserving spatial re- lationships within each band (see Fig. 1). The augmentation process is summarized in Fig.4 and described in the following. First, we com- pute the Wavelet Transform. Then the baseband, the vertical and horizontal frequency bands of the fake frame are substituted with those of the real counterpart. Finally, the inverse Wavelet Transform is applied. We adopt the Fully Separable Wavelet Transform (FSWT), which offers an anisotropic representation that more effectively localizes frequencies relative to horizomtal and vertical discontinuities compared to standard wavelets [76, 59]. which a property particularly useful our task. We leverage the Haar wavelet transform [60] with three decompasition levels, resulting in the image being split into 16 frequency bands. The -wo-dimensional FSWT can be implemented by applying the multilevel decomposition separately along the rows and columns of each frame of the video. At each decomposition level i, the wavelet transform recursively splits the lowest-freae freeuscy band along each dimension into two sub-bands. This is achieved using two convolutions with a stride of 2:
5005
Wavelet Replacement
Real
5000
2.
c
Fake
Figure 4: For each real video, four corresponding fake versions are generated: one with all fake subbands and others with selective replacements, such the base- band or specific subbands substituted with their real counterparts. Notahly, diagonal mid-to-high frequency subbands are never replaced: this is to teach the detector do without the traces brought by one or the other low- frequency subbands and hence focus on such traces.
= = 8, hy
J. = ====_11 8, hHg
(5)
where @, denotes the stride convolution operation, hr and hu the kernels of two convolutions. At each level i, the low frequency band, 2. represents a low-resolution approximation of the input initialized with the original data at the first iteration. The high frequency band, x captures detail information initially present in the higher resolution data. In similar way, the inverse wavelet decomposition is based on transposed convolutions:
x = of 8" hy + 1 8 hgg
where 80 denotes the transposed convolution operation with a stride of 2. Wavelet decomposition also offers computational advantages, its structure is well-suited for efficient implementation on

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GPUs and also has complexity of (n) less than the On (ng( of the Fourier transform. The two kernels for the Haar wavelet are: hy v2 [1
1] hR
v2 1]. 1/2
4 Results
4.1 Experimental setup
Architecture. As already discussed previously, do not propose new architecture and leverage the power of large pretrained models, that have already shown their potential for the detection of synthetic media [56, 20, 52, 71, 12]. In particular, we consider a DINOv2 model with registers [57, 22] trained end-to-end and frame-by-frame. The final decision is made by averaging the logit scores of 64 frames. Additional details about the model we used and implementation
described in Section D.
Training data. To test for the generalization ability of our method, we train the model using one single synthetic generator. This is common practice in forensies, reflecting the realistic scenario where generative architectures are unknown at test time. We leverage the Pyramid Flow model since it can generate synthetic videos with no clear visible errors and it was possible to regenerate artificial synthetic data through reconstruction (see Section 3.2). Real videos come from the Panda70M dataset [14]. Overall, training/ / validation datasets include 4,200 / 900 videos. Pairs of real and generated videos are carefully constructed using identical prompts to reduce content bias. To avoid format bias, all generated videos compressed using the same codec as the real videos, i.e. H.264, as suggested in [31]. More information on the training data are included in the appendix.
Test data. We benchmark our model on the publicly available Gen Video dataset [12] which, includes videos generated by several models for a total of around 20k real and fake videos. We decided not to use the Diffusion Video Forensics (DVF) dataset [71], since real videos are compressed using MPEG-4 Part2, while fake videos are mostly compressed using H.264. This difference in format introduces bias that can be exploited if the detector is not properly trained, leading to incorrect performance evaluation [31]. To test on more recent models we created a dataset of 2,400 videos including the following generators Allegro [95], Cog Video X1.5 [86], Mochi-1 [73], OpenSora-
[45], Sora [8]. NOVA [25] and FLUX [29] (more details in the appendix). It is worth noting that
NOVA does not model a joint distribution as diffusion models do; instead, it adopts non-quantized autoregressive formulation. For this reason, it represents an excellent test of a detector's 's ability to generalize to different generation strategies. Also for the test data, real and generated video pairs share identical prompts and all synthetic videos are compressed with H.264 (same codec as for real videos) to avoid any format bias.
Metries. Following previous works, we evaluate performance using the Area Under the Curve (AUC), which does not need to set decision threshold, and balanced accuracy, which accounts for both false alarms and missed detections. For balanced accuracy, the decision threshold is set at 0.5. We 5% false alarm rate (Pd@5%) 5%), the balanced Negative
also report the probability of detection Log-Likelihood (NLL)) [63], and binary Expected Calibration Error (ECE) [58]. The former provides insight into the false alarm behavior, which is critical in forensic applications. The latter measures are calibration metrics and indicate how much the model's predicted probability scores differ from the true probabilities and quantifies the generalization ability of the model.
4.2 Comparison with the State-of-The-Art
We compare our proposed method against several SoTA approaches. Among these, DMID [19], UnivFD [56], RINE [42], FreqNet [72], and FIRE [16] were originally developed for the detection of synthetic images. Others, AIGVDet [3], DeMamba [12], and MM-Det [71], were specifically designed for detecting Al Al-generated videos. To ensure fair comparison, we include only methods with publicly available code and/or pre-trained models. All models are trained on the datasets used in their respective original publications: ProGAN for UnivFD and FreqNet, Latent Diffusion for DMID and RINE, ADM for FIRE, Stable Video Diffusion for MM-Det, Moonvalley for AIGVDet, and videos from ten generators (ZeroScope, 12VGen-XL, Stable Diffusion, Stable Video Diffusion, VideoCrafter, Pika, DynamiCrafter, SEINE, Latte, OpenSora) for DeMamba.
Analysis with methods trained on their original datasets. Balanced accuracy results across all tested generators are reported in Table 1, while AUC and Pd@5 scores are presented more concisely in Fig.5. We begin our analysis with the average results, shown in the last column of Tab. The
7

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AUC
Pd
bAcc
Allegg
Al
Masmodley
Eogut X1.5
Mocroadly
Coglit X1.5
Hoomvllly
Cogli X1.5
Show
Sorn
Show
Op
Morph St
Hochi-3 Marph St.
Mochi-1 Morph St.
Moch-1
Lakte
Sora
LaVie
Sora
LeVie
50ro
HotShot
Flx
HoEShot
HotShot
Gon2
Cratter
Gen2
Cratter
Gen2
Cratter
DMID
UnivFD
FregNet
AIGVDet
DeMamba
MM-Det
Ours
Figure 5: Comparison with SoTA methods on 16 generative models across different evaluation metrics.
proposed method achieves average balanced accuracy of 94.3%, outperforming DeMamba, the second-best method, by ten percentage points. As expected, methods originally developed for image detection are not competitive in this setting, like UnivFD and Freq However, also the video- oriented AIGVDet lags nearly 20 percentage points behind our proposed method. The performance of MM-Det, falling below random guessing, may seem especially surprising. However, this outcome is primarily due to the biased DVF (Diffusion Video Forensics) dataset used for its original training. which leads a misleading assessment. Turning to results on the individual generators, a clear divide can be observed between the older ones, included in the Gen Video dataset, and the more recent ones added here. Several detectors exhibit a very good performance on Video (DeMamba the top detector on those data), which drops substantially on the newer generators, highlighting limited generalization capability. In contrast, the proposed method performs consistently well across all generators, with isolated dips on Videi X1.5 and on Hot Shot.
Analysis with methods re-trained on Pyramid Flow. To demonstrate that the observed performance gap is not due to the inclusion of the recent Pyramid Flow model in training, we retrained all methods using videos generated by Pyramid Flow. The resulting accuracy metrics are reported in Table 2 (other evaluation metrics are included in the appendix). Under this new training condition, we observe significant changes in performance; however, even with re-training, our method is still better than SoTA methods by large margin. Results become generally more consistent across both older and newer generators, and in some cases, most notably for MM-Det, substantially better than before. The most striking outcome, however, is that -oineeer methods outperform aideo-oreented ones and by a notable margin. This is an unexpected result and highlights that the video- based detectors are not able to fully exploit the advantage of operating in 3D. We can observe that other frequency- -based approaches, such as Net and FIRE, struggle to achieve good results even if trained on our same dataset. This highlights the importance to properly take into account the influence of the video codec in the proposed strategy. Finally, we also note that a few abrupt drops in performance (e.g., AIGVDet falling from 99.6% to 50.0% on Moon Valley, and DeMamba from 97.4% to 62.0% on Hot Shot) have a significant impact on the averages. These anomalies may be attributed to biases introduced during training, however this also requires further investigation.
Table 1: Comparison terms of balanced accuracy with SoTA methods trained on their original datasets.
Recent Generalors (2024-25 25 years)
Veneideo (2022-23 years)
Method
Allegm CogV. X1.5
OSora Plan
Mochil Nova
Sora
Flux
Crafter
Gen2
Hot Shot
LaVie
Model
Morph Stu.
Show!
Moon valley
AVG
Scape
DMID
58.7
57.3
68.7
51.8
58.8
65.8
63.5
99.0
73.4
54.1
94.2
62.9
70.7
62.6
64.1
67.0
UnivFD
49.3
52.3
49.7
51.0
59.8
48.3
49,0
58.0
51.9
53.5
57.2
63.3
55.6
52.7
54.2
53.7
RINE
63.3
62.7
79.2
66.5
53.2
49,3
49.3
98.3
89.1
66.0
96.7
76.6
84.0
91.8
85.7
74.1
FreqNet
66.8
45.0
68.8
52.2
40.5
54,2
43.5
47.9
37.2
27.7
47.7
38.0
46.5
46.2
50.9
47.5
FIRE
51.5
49.8
51.7
51.5
51.5
51.3
51.3
49.4
49.1
47.6
48.6
46.6
48.8
47.3
48.9
49.7
AIGVDet
64.8
62.0
55.3
67.7
73.5
82.2
81.2
95.6
90.0
79.1
58.5
59.5
87.2
51.7
99.6
73.9
DeMamba
56.2
90.8
83.7
77.2
70.0
56.5
56.8
98.4
98.3
97.4
97.6
80.1
98.4
97.9
98.3
84.0
MM-Det
49.5
1.3
49.8
48.2
48.2
47.8
49.3
49.9 9
49.3
0.1
50.1
44.6
50.0
50.1
48.4
42.4
Ours
91.5
85.0
97.0
93.5
93.2
98.5
98.0
98.3
98.8
81.4
95.5
97.1
97.0
92.1
98.4
94.3
8

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Table 2: Comparison in terms of balanced accuracy with SoTA methods re-trained using Pyramid Flow (*).
Recent Generators (2014-25 years)
VenVideo (2022-23 years)
Method
Allegro CogV. X1.5
OSora Plan
Mochil Nova
Sora
Flux
Crafter
Geaa
Hot Shot
LaVie
Model Scope
Marph
Show1
Moon valley
AVG
DMID
77.3
72.0
91.2
75.8
89.2
89.7
93.7
94.6
75.1
76.9
81.0
73.6
97.0
83.8
UnivFD
76.3
63.8
74.5
71.5
75.2
88.7
80.3
90.4
92.8
66.6
84.9
80.6
85.7
79.4
95.0
80.4
RINE
90.7
64.3
89,8
77.3
83.3
81.8
80.8
94.5
95.2
76.6
89.6
87.2
89.3
80.4
97.2
85.2
ProgNet*
84.3
65,5
86,3
77.5
69.0
64.5
56.5
52.1
58.5
42.4
64.7
42.8
56.5
63.0
65.0
63.3
FIRE
75.3
65.8
63.3
72.8
74,8
64.0
73.7
83,2
88.0
73.8
75.6
75.0
81.4
76.0
93.1
75.7
AIGVDet*
75.7
60.8
50,8
73.3
70.7
68.5
67.5
87.7
85,5
54.0
50.4
54.1
78.5
50.0
50,0
65.2
DeMamba
83.3
61.3
91.8
79.2
68.7
73.0
76.0
87.5
92.2
62.0
79.6
68.6
83.8
72.6
92.4
78.1
MM-Dee
56.8
49.8
71.5
70.5
70.5
76.7
72.8
84.5
87.2
40.5
85.8
63.3
76.5
80.8
87.0
71.6
Ours
91.5
85.0
97.0
93.5
93.2
98.5
98.0
98.3
98.8
81.4
95.5
97.1
97.0
92,1
98.4
94.3
Table 3: Influence of the augmentation.
Table 4: Influence of the backbone.
AUCC/bAset 1
augment
Allegro
Vidaoooo X1.5
Mochi-1
OpenSom- Plan
AVG
Network
Allegro
Vodaoo X1.5
Mochi-l
OpenSom- Plan
AVG
94.7/682
91.3/602
94.97555
98.07 80.0
94.7/110
DIND eite
98.6/91.5
97.2/85.0
99.19935
998/970
98.7:9188
87.8
95.0/ 782
98.9/90.
9944/958
97.6/88.2
DINOv2
76.6/79.2
77.5/208
886/8117
916/8335
86.1
MixUp
96.77
94.8
98.6 87.5
99.3/96.3
97.3786.
Clip
97.2/673
96.67993
99./92277
98.781155
CutMix
97.8/ 57.7
95.8/ 80.3
96.9/92.3
99.3/94.3
/8.9
CliptP
91.8/ 70.8
89.3/67.5
67.8/698
94.1 /76.5
907//112
WaveRop
99.8/97.0
Hiera video
98.5/65.8
94.7/65.3
99.4/81.
9666/73.2
NLL /CCE
L/LC/
1.95/ 3309
2.73/339
1.58/.244
1.03/ F196
1.827.284
DENOV2 ele
0.28/.076
0.53/ .140
0.19/ .156
0.07/
/.074
0.50/
0.88/
0.28/ 8844
0.15/ 833
0.49
DINO+2
0.58/ 0,50/.110
0.49 1.101
MixUp
0,35 / .106
0.71/.199
197
0.24/ .894
0.12/ 1137
7.109
Clip c2e
0,52/ [41
1,54/ .312
0.92
200
0.21 /0073
0.507.184
CuMMix
0.38
0.63/1885
0.22/
0.15
0.35 1.10
C.ipt.P
0.60/-23
0.45/.173
0
Wavekep
0.28/ / .076
0.53/.140
/140 0.19/005
0.07 .023
/.074
Hiara video
0.66/ 2004
1.66/ 339
1.54/ 538
/179
1.09/.265
4.3 Ablation study
Here we present our ablation study, where we analyze our method with different augmentations, backbones and training data.
Influence of augmentation. Table 3 shows the impact of different augmentation strategies, using always the same backbone, DINOv2 [57, 22] trained in end-to-end manner. In the first row, only a standard form of augmentation is used in training, including compression, blurring and resizing, operations typically carried out in forensies applications. Then, starting with the second row, reconstructed fake videos paired with real ones are included (marked by a check in the + +Re. column), and new forms of augmentation are added: MixUp [91]. CutMix [90], and finally our wavelet-band replacement (WaveRep). On average, across all generators, it appears that the inclusion of artificial fake videos in training (second row) ensures significant gain in performance under all metrics, AUC and bAcc in the top part of the table, and NLL and ECE in the bottom. Then, the computer dision-oriente forms of augmentation, Mix and CutMix, provide very limited additional improvements, if any. On the contrary, the proposed WaveRep provides a further performance boost, driving AUC to almost 99% and bAce past 90%.
Influence of the backbone. Next, we fix the training setup, including artificial fake videos and adding the WaveRep augmentation on top of the basic configuration, and analyze performance across different backbone architectures, with results reported in Table 4. The best results for each column (considering a margin of 1%) are highlighted in bold. Focusing on the average performance metrics, we observe that the end-to-end training (c2c) always performs significantly better than linear probing (LP) versions of both DINOv2 and Clip [64]. In the former the entire network is fine-tuned, while only a linear layer on the final features is learned for the latter. We hypothesize that the semantics-orie orieette pretraining of these models is not well suited to our forensic application, highlighting the need for a deeper adaptation. Excluding the LP variants, DINOv2 stands out as the clearly preferable option across all metrics. In general, the variability in AUC across the variants is more limited, likely because scores are already very close to 100%. but DINOV2 maintains a consistent advantage also compared to a spatial-temporal backbone based on Hiera [67], that works on clips of 16 frames, and the final decision is obtained by averaging the logits across all clips.

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Table 5: Performance by varying the number of generators during training in terms of AUC and balanced Accuracy, For each row, the results for the generators used training are crossed-out. The average values not include crossed-oul results. We compare DINOv2 network with and without our approach.
Recent Generators (2024-25 years)
aug.
Pyramid Flow
CogV. X1.5
Allegro
OpenSora- Plan
Mochi-1
Nova
Sora
Flux
AVG
400./ /992
91.3 60.2
94.7 / 68.2
98.0/ 80.0
94.9/ 75.5
99.0/877
95.7/69.5
96,8/ 72.0
95.8 73.3
400. / /9.3
97.2 / 85.0
98.6/91.5 99.5
99.8 97.0
93.5
99.3/93.3
99.9/ 98.5
99.9 / 98.0
99.1 93.8
/9.55
99.8 1888/99 398.3
94.9/ 80.5
98.7 94.7
96.5 / 86.5
99.4 /9.5
94.4/ 77.5
96.8 / 88.2
96.8/ 87.3
2
V
400. /9.3 400: / /9933
99.1 94.3
99.8/
96.5
99.6 93.5
99.9 98.7
99.9 97.8
99.9 / 98.8
99.7 96.6
400-/98.4
99-8 / 197-7 99.9/
98-0
400-/
1983
98.0 89.5
99.9 98.2
99.4/ 95.2 98.1 89.88
98.8 93.2
V
400./ 100, / 98-7
100, / 99-2
400-/ 99922
99.8 / 97.3
100. / 99.0
100. 99.0
100. / 99.2
99.9 / 98.6
Influence of number of generators in training. In this section, evaluate the influence of the number of generators included during training. More specifically, beyond Pyramid Flow, we also add Cog Video X1.5 (two generators) and another setting with four models by adding Allegro and OpenSora Plan. Note that for all these models we considered fake videos reconstructed with the same autoencoder used during generation. Results of our method with and without the wavelet-based augmentation on recent generative models are presented in Table 5, where we crossed-out the results for the generators included during training. We can observe a consistent improvement with the increase of the number of generators. Our proposed augmentation also guarantees a significant improvement especially when few models are considered, though even with four generators there is an increase of around 5% in terms of balanced accuracy. Finally, we want to highlight that our approach ranked first on the SAFE Synthetic Video Detection Challenge 2025 [68]. This challenge simulates a realistic scenario where no information about the test models are known in advance and videos vary in resolution, bitrate, frame rate, and length. This confirms the advantage of our training data and augmentation strategy to ensure good generalization ability.
Discussion
Conclusions. In this paper, we propose a novel augmentation strategy for the detection of AI- generated videos. We begin by identifying key forensic cues that are robust to compression artifacts and consistently present across different generative models. Our approach is motivated by the obser- vation that vertical and horizontal frequency components are particularly susceptible to degradation due to block-based compression algorithms commonly employed in vidco codecs. Through extensive experiments, we demonstrate that this simple augmentation improves detection performance even if only one single generator is included during training. Our findings highlight the importance of working on the training paradigm and of injecting carefully curated domain specific knowledge, which can yield greater benefit than the design of more complex detection architectures. We hope this work encourages further exploration of principled training strategies and the discovery of universally present discriminative forensic traces within the forensic community.
Limitations. The method proposed in this work incorporates forensic-oriented augmentation strategy that encourages the detector to focus on the most relevant spatial traces. However, it is not explicitly designed to capture temporal artifacts. Even if our approach outperforms existing methods that leverage temporal information, we believe it is essential to systematically investigate and characterize the most discriminative forensic cues present in the temporal domain. A deeper understanding of such artifacts could lead to detectors that more effectively exploit spatial and temporal traces. Furthermore, our method was trained using fake videos generated by the Pyramid Flow model, selected for its ability to produce high-quality synthetic content with minimal perceptible visual artifacts. This design choice allowed us to focus on subtle, low-level traces rather than easily detectable flaws. However, as new generative models emerge, potentially relying on entirely different synthesis paradigms, the current approach may struggle to generalize. Although the core principles may still be applicable, the method itself may require adaptation retraining.
10

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Acknowledgments and Disclosure of Funding
This work has received funding from the European Union under the Horizon Europe vera.ai project, Grant Agreement number 101070093, and was partially supported by SERICS (PE00000014) under the MUR National Recovery and Resilience Plan, funded by the European Union NexiGenerationEU. We thank David Luebke for early discussions on the project.
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15

---

Supplementary Material
Real video
Allegro
CogVideo X1.5
Mochi-1
Open-Sora Plan
NOVA
Sora
FLUX
*A group of scuba divers swimming in coral reef with colorful tropical fish."
Figure 6: Examples of video from our proposed test datasct generated with recent state of the art generators.
A Dataset
To conduct experiments, created dataset of synthetic videos generated using recent state-of- the-art models. For the real data, we relied on the Panda70M dataset [14], which provides access to YouTuub video URLs, precise timestamps for captioned clips, accompanying captions and relevance score indicating how well each clip aligns with its caption. We identify clips with favorable characteristics, excluding those that are screen recordings, contain static content, or exhibit minimal camera movement. To ensure high-quality and diverse dataset, we selected only such type of clips, limiting the choice a single clip per Tube video. When multiple clips were available, chose the one that was at least five seconds long and had the highest alignment score.
For our training, selected 1, 700 real videos from the Panda70M dataset and used their captions to generate synthetic samples with one single generator Pyramid Flow [38] (version with 768p miniflux weights). We further augmented the synthetic by applying the Pyramid Flow Variational Autoencoder (VAE) to the original real videos, resulting in 1, 700 more synthetic videos. The full set of 5, 100 videos split into 4, 200 for training and 900 for validation. For evaluation, selected 300 real videos and used their captions to generate synthetic counterparts using five recent video generation models: Allegro [95]. Mochi-1 [73], Cog VideoX [86] (with Cog VideoXI.5-SB weights), NOVA [25], and Open-Sora Plan [45] (with 1.3B weights). To guarantee consistency with the real videos, we compressed all generated videos using exactly the same codec of the real videos, i.e. Advanced Video Coding (H.264). Specifically, we used the Main @ L3.1 codec profile and Constant Rate Factor (CRF) randomly sampled between 16 and 30.
Additionally, the same captions were used to produce other synthetic videos via two online platforms: Sora [8] and FLUUX [29]. Overall, using 7 generators,
created 2, 100 synthetic videos with frame-rate that goes from 12 fps (NOVA) to 30 fps (Mochi-1, Sora) and a spatial resolution that varies from 640 352 pixels (Open-Sora Plan) to 1360 768 pixels (Cog VideoX). Some examples can be seen in 6, Fig. and Fig.1.
B Artifacts analysis
In this Section present additional analysis of the low-level artifacts for other synthetic generators. In particular, in 7, we show the close up of spatial and temporal-spatial power spectra before (top) and after (bottom) compression for additional synthetic generators. In all cases, forensic artifacts (even for NOVA that relies on an autoregressive model) that are significantly reduced after compression. However, artifacts remain visible, particularly along the diagonal directions. It also worth noting that temporal artifacts are less prominent, and the peaks along the vertical and horizontal directions tend overlap with those introduced by the compression algorithm.
observe strong
To further support our conjecture that the mid-high frequencies along the diagonal directions are more discriminative, compare real and reconstructed videos in the frequency domain. Reconstructed
16

---

Allegro
Video X1.5
NOVA
Figure 7: For each generator (Allegro, CogVideo X1.5, NOVA), show the close up of its spatial power spectrum Syz (u, v) (left), and the close up of its temporal-spatial power spectra Sye (u, w) (right), both before compression (top) and after compression (bottom),
Figure 8: Average distance in the frequency domain defined in eq.7, before compression (left) and after compression (right). The distance is computed between the real and the reconstructed video generated by the autocncoder of Pyramid Flow [38]. In this way we can show the artifacts the model that are not influenced by the semantic content. The distance is nearly zero at the low frequencies and along the vertical and horizontal directions, much larger at mid-high frequencies along diagonal directions, when videos are compressed.
videos obtained with the same autoencoder used during generation (see Section 3.2), hence they share the same content as real videos, but embed the traces related to the generation architecture. Such traces can be highlighted by computing the following distance:
N Σ Σ
Xi(u, v, w) X,(u, v,
d(u, v) = N
E. (u, 2)
where Xı( v, w) and X w) are the 3d-Fourier transforms of i-th real video and its recon- struction, respectively, while N the number of videos, that is equal to 100 in this experiment. In this comparison, we take into account the fact that small differences in low-energy regions of the spectrum are more significant than the same differences in high-energy regions. Therefore, we normalize the sum of squared differences by the energy of the real data at each spatial frequency. We show the average distance d(u, v) on the left side of Fig.8, while the right side displays the same quantity evaluated after compressing the reconstructed videos using the same codec as for the real ones. Before compression (Fig.8, left) the reconstructed video shows significant deviation from the real video the mid-high frequencies, while horizontal and vertical frequencies appear to be less useful for detection. After compression (Fig. 8, right), the distances are reduced, but the diagonal mid-high frequency components are still discriminative.
To verify that the proposed detector leverages these mid-high frequencies establish if video is fake or not, we conducted suitable toy experiment: at test time we modified the real videos and injected the mid-high frequencies of fully synthetic videos into them. We observe a performance drop on this test set. In particular, the True Negative Rate (TNR), which is the probability that real videos are correctly classified as real, goes from 98.7 to 0.2 (on average). This clearly shows that the detector inverts its decision.
17

---

Table 6: Performance by varying L (number of wavelet decomposition levels).
1/boe/ 1
NLL 4 J/ECE
L
Allegro
CogVidoo X1.5
Mochi-1
OpenSora- Plan
AVG
Allegro
CogVideo X1.5
Mochi-1
OpenSora- Plan
AVG
98.6 / 90.0
96.5/ 81.7
98.9/91.5 99.8/ / 96.0
98.4/ / 89.8
0.29 / .99
0.60/.174
0.24/ .799 0.08/ / .031
0.30/ .095
98.6 / 91.5
97.2/ 85.0
99,1 /9.5
99.8 / 97.0
98.7 /918
0.28 / .076
0.53/.140
0.19/ .056
0.07 / .023
0.27 / .074
9 88.8
95.7 /1.5
98.2/ 89.3
94.5
97.7/88.5
0.39 /.102 0.67 /667
/ .045 0.39 0.39/.101
Table 7: Comparison in terms of AUC with SoTA methods both trained on their original datasets and re-trained using Pyramid Flow (+). Bold underlines the best performance for each column with margin of I 1%.
Recent Generators (2024-25 years)
Gen (2022-23 years)
AUC
Allegro CogV. X1.5
OSora Mochil Nova
Sora
Flux
Crafter
Gea2
Hot Sbot
LaVic
Model
Morph Stu.
Showl
Moon valley
AVG
Plane
Scope
DMID
93.2
93.2
96.4
78.8
94,1
99.8
100.
99.7
98.9
99.6
99.7
99.9
96.7
UnivED
47.7
60.1
42.8
53.3
32.4
40.6
88.0
74.8
77.5
86.3
76.4
79.6
67.6
RINE
83.3
82.7
92.2
82.0
11.0
0.7
1.0
99.9
99.5
95.4
99.5
99.0
99.3
99.6
99.4
76.3
ProgNet
75.1
43.1
80,5
52.8
37.3
56.9
39.8
57.4
30,8
23.2
55.7
42.5
54.2
48.7
58.1
50.4
FIRE
56.0
51.2
93.0
85.8
79.7
40.8
46.8
36.9
23.0
35.1
62.0
25.7
24.3
40.1
18.6
47.9
DMID
99.5
98.7
99.9
96.6
99.4
99.3
99.8
98.8
99.8
93.3
97.6
96.9
97.9
97.2
99.6
98.3
UnivED
93.5
83.6
92.8
89.3
93.5
97.3
94.4
97.2
98.0
81.7
94.0
91.7
94.6
90.6
98.9
92.7
RINE
99.7
92.3
99.1
96.8
98.6
97.5
97.1
98.6
98.9
90.7
97.5
97.0
97.5
93.8
99.7
97.0
Fre
91.6
72.3
94.3
84.3
79.2
73.7
69.6
53.5
63.6
41.1
76.0
42.4
59.8
69.2
71.8
69.5
FIRE*
83.9
68.6
66.4
79.8
83.4
70.3
80.8
92.2
95.0
84.3
85.1
84.4
91.0
83.2
97.4
83.0
AIGVDer
80.7
76.0
85.4
78.5
88.0
89.4
89.1
98.0
95.9
97.0
96.4
73.5
94.0
80.6
99.9
88.2
DeMamba
78.7
97.1
95.6
86.6
82,8
65.8
60.5
100.
100.
99.6
99.7
94.7
100.
99.9
100.
90.7
MM-Det
53.9
0.0
47.7
44.3
41.1
44.8
53.8
46.5
43.1
49.7
31.9
49.6
51.0
37.9
39.7
AIGVDer*
92.3
91.2
42.4
91.4
94.8
88.7
91.4
99.9
100.
98.6
95.0
99.3
99.9
88.2
100.
91.5
DeMamba
97.9
88.5
99.2
93.8
90.8
94.2
95.3
94.7
97.2
70.0
92.3
78.2
92.9
85.7
96.3
91.1
MM-Der*
89.2
0.0
97.2
96.5
97.3
97.0
93.3
95.4
94.3
84.5
89.1
95.0
79.7
Ours
98.6
97.2
99.8
99.1
99.9
99.9
99.8
100.
91.0
97.7
99.4
99.3
97.1
99.9
98.5
C
Additional results
Ablation. In Table 6, We conducted a study to assess the impact of the number of decomposition levels of the wavelet transform implemented in our augmentation strategy. Specifically, we evaluated configurations with 2, 3, and 4 levels of decomposition. The results indicate that the choice of 3 decomposition levels yields the better performance across multiple metrics. The difference is more marked if we look at the Accuracy than the AUC. Even NLL and especially ECE show an advantage in this situation, which confirms that this choice helps to achieve more calibrated results.
SoTA comparison. In Table 1 and 2 of the main paper, we report the comparison with SoTA methods only in terms of balanced Accuracy. Here, in Tables 7, 8, and 10, show the performance using the other four metrics: AUC, Pd=5,, NLL and ECE. AUC is generally higher than the balanced Accuracy for most of the methods, with variations even superior 15% This means that fixed threshold set to 0.5 is not the best choice and proper calibration is needed. However, our approach is instead able to achieve good performance consistently over all the metries, in particular it gains an improvement on average of around 53% and 68% for NLL and ECE, respectively.
Compression robustness. We compressed the test dataset used in our ablation study with H.264 at differ- ent quality levels by varying the Constant Rate Factor (CRF). Table 11 reports results across decomposi- tion levels, showing robustness to compression (AUC always above 80%). Accuracy drops under severe compression (around 60% at CRF 32) motivating a threshold calibration procedure for low-quality com- pressed videos (calibrating with only 10 compressed validation videos raises accuracy to 76%).
Table 11: Performance by varying compression quality (CRF) and the number of wavelet decom- position levels (L)).
AUC t t/hAce n
Tw22
L.m3
CRF 20 (high quality)
97.8/83.8
97.3/84.5
96.6/
83.2
CRF 24 CRF 28
96.8/77.1
96.0/78.3
95.2/ 78.2
93.9/67.9
92.5/69.1
91.1/68. 68.5
CRF 32 (low quality)
88.2/59.5
85.2/59.2
82.6/ 59.3
18

---

Table 8: Comparison in terms of 5de5 with SoTA methods both trained on their original datasets and re-trained using Pyramid Flow (*). Bold underlines the hest performance for each column with margin of 1%.
Recent Generators (2024-25 years)
GenVideo (2022-23 years)
Pde5 7
Allegro CogV.
OSora Mochil Nova
Sora
Flux
Crafter
Gen2
Hot Shot
LaVie
Model
Morph Stu.
Show
Moon valley
AVG
X1.5
Plane
Scopc
DMID
73.0
76.0
82.7
40.0
72.3
92.3
99.9
100.
99.0
96.0
99.9
96.6
99.0
99.1
100.
88.4
UnivFD
3.7
10.3
4.3
7.3
26.0
1.7
3.0
59.6
31.4
40.7
52.7
77.6
52.9
35.9
40.6
29.8
39.3
73.7
46.0
0.7
0.7
99,9
98.7
77.4
98.5
97.7
98.0
98.1
99.0
65.4
ProgNot
16.7
4.8
22.2
7.5
3.0
14.8
3.6
12.7
9.6
12.4
FIRE
6.3
5.7
84.7
70.3
62.3
1.3
2.0
0.0
0.0
0.0
1.6
0.6
0.0
0.0
0.0
15.7
DMID"
97.3
94.3
99.0
87.0
97.3
98.0
98.7
95.9
99.7
81.0
90.9
88.9
90.3
85,9
99,2
93.6
UnivFD*
67.3
45.7
67.7
61.0
68.3
86.3
75.3
85.8
90.9
38.7
75.2
66.4
76.4
64.4
94,9
71.0
RINE
98.3
67.3
95.7
84.3
93.3
89.0
87.0
94.7
95,4
64.1
87.6
84,9
86.4
73.0
98.6
86.7
TregNeet
56.7
22.7
71.7
33.7
18.3
11.3
5.7
6.7
13.2
6.6
33.3
3.7
11.0
16.1
13.7
21.6
FIRE*
45.7
26.3
28.7
50.7
51.3
22.7
44.0
67.3
70.0
45.9
48.9
38.9
59.0
49.3
88.5
49.1
AIGVDet
34.7
29.0
49.3
40.7
51.3
71.7
67.7
94,1
87.3
91.1
88.3
44.1
80.7
57.1
99.8
65.8
DcMamba
21.3
88.7
81.0
62.3
50.0
22.0
22.7
100.
99,9
98.7
99.0
71.4
100.
99.4
100.
74.4
MM-Det
5.6
0.0
4.6
4.3
3.8
4.3
5.5
4.6
4.3
0.0
5.0
3.1
5.0
5.1
3.8
3.9
AIGVDer*
71.7
59.0
23.3
68.7
72.0
64.7
65.3
99.4
99.9
94.4
75.8
96.1
99.7
43.9
100.
75.6
DeMamba*
94.0
59.77
97.3
82.0
69.7
78.7
85.7
81.8
90.1
31.1
69.9
45.3
75.3
56.0
89.3
73.7
MM-Der
52.3
0.0
85.7
83.7
84.7
85.7
82.7
72.2
76.2
74.1
25.1
39.1
52.6
76.7
59.4
Ours
93.0
84.3
99.0
94.7
95.3
100.
99.7
99.1
99.9
72.9
94.3
97.9
97.3
91.0
99.5
94.5
Table 9: Comparison in terms of NLL with SoTA methods both trained on their original datasets and re- re-trained using Pyramid Flow (*). Bold underlincs the best performance for each column with margin of 1%.
Recent Generators (2024-25 years)
GenVideo (2022-23 years)
NLL
Allegro CogV. X1.5
OSora Mochil Nova
Sora
Flux
Crafter
Gen2
Hot Shot
LaVie
Model Scope
Morph
Showl
Moon valley
AVG
Plane
DMID
2.18
2.14
1.53
3.97
2.10
1.10
0.73
0.04
0.92
2.09
0.20
1.82
1.17
1.10
1.08
1.48
UnivFD
2.76
2,16
3.02
2,50
3.62
3.13
1.76
2.67
2.44
1.96
1.93
2,56
2.34
2.37
RINE
0.59
0.60
0.45
0.59
17.2
15.6
0.14
0.30
0.50
0.32
0,26
0.34
2.93
FregNet
10.6
15.6
10.2
13.6
16.8
13.7
16.8
30.5
33.0
35.5
30.6
33.6
30.7
30.7
29.5
23.4
FIRE
3,95
4.01
3.94
3.94
3.95
3.95
3.95
4.84
4.84
5.00
4.86
5.01
4.85
4.96
4.84
4.46
DMID
0.41
0.51
0.19
0.57
0.35
0.26
0.22
0.20
0.14
0.69
0.32
0.50
0.41
0.54
0.14
0.36
UnivFD*
0.53
1.00
0.56
0.71
0.54
0.27
0.46
0.22
0.19
0.88
0.37
0.48
0.35
0.52
0.14
0.48
RINE
0.24
2.61
0.42
1,27
0.68
0.95
1.11
0.24
0.19
1.46
0.38
0.45
0.38
0.97
0.07
0.76
FregNer*
0.65
1.80
0.61
1,02
1.17
1.52
1.66
2.93
2.34
4.66
2.35
4.31
2.76
2.27
1.96
2.13
FIRE+
0.51
0.77
0.82
0.62
0.54
0.70
0.56
0.39
0.29
0.66
0.63
0.65
0.43
0.68
0.20
0.56
AIGVDet
0.95
1.16
0.84
1.07
0.67
0.70
0.69
0.31
0.46
0.48
0.52
1.35
0.46
1.12
0.32
0.74
DeMamba
1.98
0.33
0.48
1.12
1.41
2.57
2.78
0.05
0.05
0.08
0.07
0.64
0.05
0.06
0.05
0.78
MM-Det
38.4
88.3
38.4
38.6
38.5
38.6
38.4
48.3
48.3
98.3
48.3
48.8
48.3
48.3
48.4
50.4
AIGVDer*
0.60
0.72
3.57
0.64
0.56
0.77
0.69
0.44
0.30
2.72
4.38
1.08
0.56
7.55
0.35
1.66
DeMambu
0.49
1.66
0.22
0.85
1.31
1.00
0.83
0.45
0.26
1,93
0.67
1.47
0.58
1.08
0.30
0.87
MM Det
3.60
50.0
1.50
1.71
1.55
0.74
0.63
1.13
0.89
0.65
7.92
Ours
0.28
0.53
0.07
0.19
0.18
0.04
0.05
0.06
0.05
0.53
0.16
0.09
0.09
0.22
0.05
0.17
Training data. In this paragraph, we evaluate the influence of training data. In Table 12, the proposed augmentation yields clear gains even with limited data (20% of the training set) and when training on
Table 12: Performance by varying the type of gen- erator and the size of the dataset during training.
/
a different generator (Cog Video instead of Pyramid Flow). With only 20% of the data, augmentation still improves balanced accuracy (+3.2%). Likewise, when replacing Pyramid Flow with Cog VideoX, we again observe a substantial boost from the proposed augmentation strategy (+6.3%)
Iraining ditt
ang
Allagno
Muchiel
OpenSooa- Pln
AVG
100% Pyr. Flow
94.7
94.97755
95.0 30,0
95.97 7466
100% pyr. Flow
96.6/91
793.5
9938
99.1/940
20% Pyr. Flow 20% Pyr Flow
94.3/70.8
93.977788
98.17
95.5/7677
97.6 58.3
98.8/92.7
99.8
98.71925
100% Cog.V XI.5 100% Cogy. X1.5
90.7/691.
90.7/72.1
94.91 78.0
92.1/73.1
98.5/
97.6/85.2
99.2/92.8
19

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Table 10: Comparison in terms of ECEE with SoTA methods both trained on their original datasets and re-trained using Pyramid Flow (*). Bold underlines the hest performance for each column with margin of 1%.
Recent Generators (2024-25 years)
GenVideo (2022-23 years)
BCB.
Allegro CogV.
OSora Mochil Nova
Sora
Flux
Crafter
Gen2
Hot Shot
LaVic
Model
Morph
Show
Moon valley
AVG
X1.5
Plane
Scopc
Stu.
DMID
.406
.406
.312
.471
.399
.334
.329
.026
.274
.366
.068
.362
.289
.347
.341
.320
UnivFD
.479
.419
.481
.438
.324
.501
.889
.398
.464
.455
.410
.348
420
.458
440
434
.149
.167
.107
.202
.208
.173
.230
ProgNot
.28
.449
.217
.470
.180
.286
.194
.198
.153
298
FIRE
.43
.492
.482
.482
.482
.484
.422
.503
.505
.520
.509
.529
.507
.522
.506
.99
DMID
.27
.254
.125
.237
.137
.140
.078
.066
.241
.220
.193
.248
.000
UnivFD*
.195
.299
.200
.229
.206
.054
.162
.039
.024
.252
.096
.142
.091
.151
012
.147
RINE
.988
.339
.110
.255
.122
.190
.188
0032
002
.202
.05
.103
.079
1622
.010
.134
TreyNett
108
.296
1006
.170
.244
.289
.361
.404
.347
.512
.312
.499
.368
.309
.289
.308
FIRE*
.86
.156
.171
.129
100
.144
.091
.069
.051
.158
.141
.168
.090
.142
.044
.116
AIGVDet
.287
.305
.264
.283
.233
.204
.206
.157
.192
.239
.230
.293
.150
.277
.230
.237
DcMamba
.400
.079
.120
2111
.274
.415
.405
.020
.019
.012
.014
.171
.020
.014
.019
.147
MM-Det
.129
.612
.126
.140
.139
.143
.129
.019
.025
518
.018
.070
.019
.018
.034
.143
AIGVDer*
230
.292
.411
.233
.243
.266
.258
.181
.162
420
.442
.305
.248
.492
.250
.26
DeMamba*
.159
.364
.074
.133
.211
.249
.224
.092
.047
.346
.667
.278
.127
.236
.050
.93
MM-Dert
421
.502
.272
.244
.292
.239
.268
.111
.089
.533
.101
.305
.170
.134
.091
.288
Ours
.076
.140
.023
.056
.058
.014
.012
.025
.029
.145
.016
.013
.012
.043
.026
.046
D Implementations details
The proposed solution uses as backbone the Vision Transformer (ViT) architecture with 4 registers [22] and pre-training based on DINOv2 strategy [57]. Specifically for the model, we use the implementation available in the PyTorch Image Models (TIMM) library followed by single fully connected layer for binary classification. The model processes an input of 36 x 36 patches, each of size 14 x 14 pixels. Training is performed using the cross-entropy loss function and the ADAM optimizer, with a learning rate of 10 a weight decay of 10-6, 60-6, and a batch size of 22. During training. we apply the augmentation strategy described in the main paper with probability of 10%. Frames are randomly cropped to match the input size of the network, and each batch contains an equal number of real and fake crops. We evaluate the balanced aceuracy the validation set every 2, 035 iterations. Training is conducted for 36 evaluation steps, and only the model weights corresponding to the best validation performance kept and used for testing. At inference, the prediction pipeline consists of the following steps (Fig. 9):
1. the first 64 frames of the video are extracted;
2. the frame is center-cropped match the network input size;
3. the network processes the frame and produces a corresponding logit value;
4. video-level prediction is computed by averaging the 64 logit values.
Finally, we report the computational times. The analysis is carried out on a hardware with two CPUs Intel(R) Xeon(R) Gold 5220 CPU @ 2.20GHz with 128GB of RAM, and a Nvidia A6000 GPU with 48GB of VRAM. The proposed method exhibits a relatively low inference time: takes around 13 seconds to analyze video of 64 frames, which is comparable with MM-Det and lower than DeMamba (21 sec) and AIGVDet (180 sec). The total training time for our method about 77 hours.
Frame by frame network
Cropping to input
ViT with registers DINOv2 pre-training
Dense Layer
Real/ Fake video score
Input Video
logit values
Figure 9: Prediction pipeline. At inference, we use up to frames per video, center-cropped to match the model input. Video-level predictions are computed by averaging logits across frames.
20

---

Open-Sora Plan
NOVA
Sora
FLUX
"A band is performing on stage under a tent in an outdoor setting."
Real video
Allegro
CogVideo X1.5
Mochi-1
Open-Sora Plan
NOVA
Sora
FLUX
"A glasses holding bottle of hot talking the camera."
Allegro
CogVideo
Open-Sora Plan
NOVA
Sora
FLUX
"A white chicken is sitting Inside of pen next to fence."
Real video
Allegro
CogVideo X1.5
Mochi-1
Open-Sora Plan
NOVA
Sora
FLUX
riding Indoor and jumping obstacles."
Figure 10: Some examples of vidcos from datasct that created with recent state of the art generators.
21

---

METAN
Open-Sora Plan
NOVA
Sora
FLUX
"A black cat with yellow eyes is sitting inside of cage."
Real video
Allegro
CoaVideo X1.5
Mochi-1
122E
Open-Sora Plan
NOVA
Sora
FLUX
"A bottle of izze desk next computer."
video
Allegro
CogVideo
Mochi-1
Open-Sora Plan
NOVA
Sora
FLUX
*A table with several trays of plants on it."
Real video
Allegro
Cogvideo X1.5
Mochi-1
Opan-Sora Plan
NOVA
Sora
FLUX
bowl peas with garnish on top."
Figure 11: Some examples of vidcos from datasct that created with recent state of the art generators.
22

---

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29


Reviews:
[{"review_id": "wDM6DxMHgd", "reviewer": "Reviewer_gFZ8", "summary": "This paper proposes a novel training paradigm for synthetic video detection using forensic-oriented data augmentation based on wavelet decomposition. The authors claim to identify discriminative features in mid-high diagonal frequencies that remain robust across different video generators against compression, and introduce wavelet-band replacement augmentation to redirect model focus from semantic artifacts to forensic cues. The method achieves 10+% bAcc improvement over existing detectors while only being trained on a single generator. The core contribution is to augment the data by preserving forensic artifacts while reducing semantic bias, enabling better cross-generator generalization.", "questions": "Minor suggestions:\n- Missing detectors based on temporal forgery cues [1], motion decomposition [7], frequency analysis [5,6], augmentation [4], attention [8], and generalization [2,3]. Not all of them need to be compared against, but the related work section lacks comparison.\n- Specifically for source generator detection for deepfake videos (line 137), there are more relevant work [7,9].\n- A real challenge (and validation) would be evaluating the proposed method and others on Deepfake-eval-2024 dataset, which is claimed to be truly in the wild. \n- Size of training/validation sets are significantly low.\n- The order of the results is weird, talking about ablation before results is not conventional. \n- Mixup and Cutmix cites can be moved to line 263.\n\n\n[1] Guo, Z., Liu, Y., Zhang, J., Zheng, H., & Shan, S. (2025). Face Forgery Video Detection via Temporal Forgery Cue Unraveling. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 7396-7405).\n\n[2] Wang, Z., Bao, J., Zhou, W., Wang, W., & Li, H. (2023). Altfreezing for more general video face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4129-4138).\n\n[3] Yan, Z., Zhao, Y., Chen, S., Guo, M., Fu, X., Yao, T., ... & Yuan, L. (2025). Generalizing deepfake video detection with plug-and-play: Video-level blending and spatiotemporal adapter tuning. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 12615-12625).\n\n[4] Yan, Z., Luo, Y., Lyu, S., Liu, Q., & Wu, B. (2024). Transcending forgery specificity with latent space augmentation for generalizable deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8984-8994).\n\n[5] Kashiani, H., Talemi, N. A., & Afghah, F. (2025). FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 8775-8785).\n\n[6] Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., & Wei, Y. (2024, March). Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 5, pp. 5052-5060).\n\n[7] Demir, I., & Çiftçi, U. A. (2024). How do deepfakes move? motion magnification for deepfake source detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 4780-4790).\n\n[8] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., & Yu, N. (2021). Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2185-2194).\n\n[9] Çiftçi, U. A., Demir, I., & Yin, L. (2024). Deepfake source detection in a heart beat. The Visual Computer, 40(4), 2733-2750.", "limitations": "Limitations of the approach are well discussed. The paper makes a valuable contribution to synthetic video detection by proposing a practical augmentation strategy that utilizes generation artifacts, claimed to be generalizable across multiple datasets and generators. The paper addresses an important problem with a technically sound approach and demonstrates promising results. With proper validation of compression robustness claims, technical details and ablation studies on the frequency bands, better comparison organization, and inclusion of recent baselines, this work could significantly advance the field's understanding of forensic-oriented detection methods.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The foundation and motivation of the paper is very clear, connected to implementation and design choices, and related with the literature. The authors' focus on mid-high diagonal frequency components aligns with established research on GAN-specific frequency signatures. The choice of wavelet decomposition is well-justified, as wavelets preserve spatial information, while enabling multi-scale frequency analysis, while also being computationally more efficient. \n\n- Forcing models to focus on intrinsic forensic cues rather than semantic differences is a strong motivation and is implemented clearly. It would have been even better if the paper documented (or referred) to the current landscape of detectors battling with this problem. \n\n- Training only on one generator and generalizing to 15 generators is an excellent evaluation for generalization, especially for a production environment. The comparison tables, though, need another pass.\n\n- Very interesting observations on the relation between compression and generative fingerprints, which leads to mid frequency components. A compression ablation study would be the cherry on the top.\n\nWeaknesses:\n\n- There needs to be a clear distinction between \"generation-specific artifacts\" vs. \"artifacts introduced by specific generators\", else the paper is trying to solve what is claimed to be solved by other methods. \n\n- There is very little information on the denoiser, which can be very influential on the results. An ablation study with/without would enhance the design choice.\n\n- To further emphasize the claims of the paper, I would be very interested to see a result where the same method is trained/tested on the training/testing set of all 15 generators. That would be a showstopper if training the same model on 15 generators performs worse than training on one augmented generator. If only Pyramid Flow training is causing such high performance, I would be excited to see the training on more generators. Then it opens the doors for yet another research question: is augmentation all we need (for df detection)? :)\n\n- While it is known that compression helps with deepfake detection evasion as an adversarial strategy, the paper needs stronger empirical validation of compression robustness claims with systematic evaluation across multiple compression standards and quality levels, possibly with different frequency decompositions for the wavelet.\n\n- I believe Tab. 3 is neither fair nor relevant for the claims of this paper. If the contribution of this paper is the new Wavelet-based augmentation method, then augmenting the training sets of these detectors and then observing their accuracy increase would be a fair comparison. If the contribution is the DINOv2 + augmented data, then it should have been trained/tested on the detectors' datasets. Tab. 4 comes closer to a fair ground, however the contribution is still unclear here: DINOv2 backbone itself is not making this difference, is it the augmented Pyramid Flow data + DINOv2? What happens if we augment the training data of all?\n\n- I would assume compared detectors to have some commonality with this detector, may it be generalization, or fingerprints, or frequency-based analysis. There are many other detectors that might have been compared which share similar motivations, like other frequency domain approaches (DCT-based methods, Fourier analysis). \n\n- While the paper identifies mid-high diagonal frequencies as discriminative, the analysis lacks depth compared to established frequency domain research. More in-depth analysis (or even classification) of spectral artifacts different generators or a quantitative frequency band analysis would ground the observations.\n\n- The wavelet-band replacement augmentation procedure needs clearer algorithmic description.", "was_revised": true, "final_justification": "Almost all of my concerns are answered. I left a suggested additions list in my response. With those 7 items (or those that can fit in the main paper) it will be an interesting and meaningful contribution to NeurIPS.", "rebuttal": "We thank the reviewer for their positive feedback on our paper. We are glad that they found that “it addresses an important problem”, its motivation is “very clear, connected to implementation”, and “related with the literature”, that our “approach is technically sound and results are promising”, our “observations on the relation between compression and generative fingerprints are very interesting” and that “training only on one generator and generalizing to 15 generators is an excellent evaluation”.\n\nIn the following, we address each question individually.\n\n**Q1: \"generation-specific artifacts\" vs. \"artifacts introduced by specific generators\".**\n\nThank you for pointing this out. We will make clearer the distinction between \"generation-specific artifacts\", which we consider to be traces related to the generation process, that are distinct from \"artifacts introduced by specific generators\", _e.g._, certain semantic visible cues, such as the lack of perspective or temporal consistency in the generated videos. \n\n**Q2: Denoiser used in visualization.**\n\nWe used the denoiser only to extract the forensic fingerprints and to motivate our method. However, we do not use a denoiser in our detector, which is only based on the wavelet transform to implement our frequency-based augmentation strategy. We will make this clearer in the paper and give more details of the denoiser.\n\n**Q3: Training on 15 generators.**\n\nThis is an interesting question. While including more generators might seem to improve performance, the answer is not straightforward. A key issue is which generators to include during training. For example, DeMamba [12] was trained on 10 generators (from 2023–2024) and performs well on older models but struggles to generalize to newer ones (see Table 3). This suggests that both the choice and the number of generators remain open research questions.\n\nIt is also not feasible for us to apply our data augmentation procedure to many generators that require reconstruction with the same autoencoder used during generation. Nevertheless, as one datapoint, we conducted an experiment, wherein, we compared the model trained using two generators (Pyramid Flow (P) + CogVideoX (C)) with the same model trained using one single generator (P or C) and their corresponding versions with our wavelet-based augmentation. In the following table we show both AUC and balanced accuracy (bACC). We observe that using one single generator with its simpler design and our augmentation gives better results than including two different generators (92.5 and 95.3 vs 87.3 in terms of bACC). Arguably, adding more generators may potentially change these results. Nevertheless, it shows one quantified datapoint.\n\n| AUC / bAcc  |  Allegro  |  Mochi-1  |OpenSora-Pl|    Nova   |    Sora   |    Flux   |    AVG    |\n|:------------|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|\n| C           | 90.7/69.3 | 90.7/72.0 | 94.9/78.0 | 99.1/94.2 | 89.0/63.5 | 94.6/77.5 | 93.2/75.8 |\n| C + our aug.| 98.5/88.8 | 97.6/85.2 | 99.2/92.8 | 99.8/95.8 | 99.8/96.8 | 99.7/95.3 | 99.1/92.5 |\n| P           | 94.7/68.2 | 94.9/75.5 | 98.0/80.0 | 99.0/87.7 | 95.7/69.5 | 96.8/72.0 | 96.5/75.5 |\n| P + our aug.| 98.6/91.5 | 99.1/93.5 | 99.8/97.0 | 99.3/93.2 | 99.9/98.5 | 99.9/98.0 | 99.4/95.3 |\n| P + C       | 94.9/80.5 | 96.5/86.5 | 98.7/94.7 | 99.4/96.5 | 94.4/77.5 | 96.8/88.2 | 96.8/87.3 |\n\n**Q4: Compression robustness.**\n\nWe compressed the test dataset used in our ablation study (both real and fake videos) with H.264 at different quality levels by varying the Constant Rate Factor (CRF). Results using different wavelet decomposition levels are presented in the Table below and show that our method is robust to different compression levels with AUC always remaining above 80%. \n\n|     AUC / bAcc        |   2 levels  |   3 levels  |   4 levels  |\n|:----------------------|:-----------:|:-----------:|:-----------:|\n| CRF 20 (high quality) | 97.8 / 83.8 | 97.3 / 84.5 | 96.6 / 83.2 |\n| CRF 24                | 96.8 / 77.1 | 96.0 / 78.3 | 95.2 / 78.2 |\n| CRF 28                | 93.9 / 67.9 | 92.5 / 69.1 | 91.1 / 68.5 |\n| CRF 32 (low quality)  | 88.2 / 59.5 | 85.2 / 59.2 | 82.6 / 59.3 |\n\nThe accuracy gets significantly lower when the quality decreases (around 60% in the last row, CRF 32) and this clearly indicates the need for a calibration procedure for low-quality compressed videos to estimate the optimal threshold value. By using only 10 compressed videos from the validation set for threshold calibration, we noted an increase in accuracy from 60% to 76%. We will include these results in the revised version of our paper.\n\n**Q5: Augmenting SoTA training data.**\n\nWe understand this critique. However, it is not always obvious as to how to perform this analysis, since the training sets of current detectors can include generators whose architectures are not disclosed (e.g., Moonvalley, Pika), which means that we cannot apply our procedure that requires data reconstruction with the same autoencoder used during generation. In addition, some prior methods employ high-level semantic traces for detection. Hence our augmentation, which is focused on low-level traces makes less sense in their context. \n\nNevertheless, we re-trained the method DMID [18] on our dataset and obtained an average improvement of +1.5 in AUC and +1.9 in Accuracy on the more recent generators (2024-25 years) by including our augmentation strategy. We will add more such comparisons with other prior datasets in the revised version of our paper.\n\n**Q6: Other frequency-based detectors.**\n\nThere are indeed other methods that leverage frequency domain analysis to design an effective forensic detector. Some approaches aim at forcing the detector to focus on high-frequency information [A, B], while others exploit discrepancies in the whole frequency spectrum that can be achieved, _e.g._, by concatenating frequency-based components in the DCT domain [C]. A more recent work is based on the observation that the reconstruction error exhibits stronger mid-frequency components for real images versus for generated images [D]. In our approach, instead, we guide the detector to focus on diagonal mid-high frequencies, as video compression artifacts overlap with forensic cues in vertical/horizontal directions. Unlike [E], which uses frequency-domain Mixup without targeting model-specific cues, our method focuses on artifacts that persist after compression.\n\nWe will add a paragraph in the Related Work section of the paper where we will explicitly make clear the differences between such approaches and our method. Furthermore, we will add comparisons with those works whose code is publicly available. We already performed some comparisons. Results are presented below in terms of AUC/bAcc both on their training data and by re-training on Pyramid Flow. Our method guarantees better performance than these detectors even when trained on the same dataset.\n\n| AUC / bAcc   |     Allegro |  CogvideoX  | OpenSora-Pl.|     Mochi-1 |        AVG  |\n|:-------------|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| [A] FreqNet  | 75.7 / 66.7 | 42.9 / 45.5 | 81.0 / 68.3 | 54.4 / 52.8 | 63.5 / 58.3 |\n| [A] FreqNet* | 91.5 / 84.7 | 72.1 / 65.7 | 94.7 / 86.0 | 84.5 / 78.0 | 85.7 / 78.6 |\n| [D] Fire     | 60.5 / 51.3 | 53.8 / 49.8 | 88.5 / 51.5 | 81.8 / 51.3 | 71.1 / 51.0 |\n| [D] Fire*    | 84.0 / 75.5 | 68.6 / 66.0 | 66.6 / 63.5 | 79.7 / 73.0 | 74.8 / 69.5 |\n|     Ours     | 98.6 / 91.5 | 97.2 / 85.0 | 99.8 / 97.0 | 99.1 / 93.5 | 98.7 / 91.8 |\n(*) re-trained on Pyramid Flow\n\nReferences:  \n[A] Tan et al. Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning, AAAI 2024  \n[B] Wang et al. Dynamic Graph Learning with Content-guided Spatial-Frequency Relation Reasoning for Deepfake Detection, CVPR 2023  \n[C] Qian et al. Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues, ECCV 2020  \n[D] Chu et al. FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error, CVPR \n2025  \n[E] Kashiani et al. FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing, CVPR 2025\n\n**Q7: More in-depth analysis.**\n\nTo support the conjecture that the mid-high diagonal frequencies are more discriminative, we conducted a further analysis in Section B of the Appendix, where we compare real and reconstructed videos in the frequency domain by computing the distance defined in equation 7 (see supplemental material). A higher value for this distance indicates a greater difference between the real videos and the reconstructed ones, and therefore a more discriminative capability of the corresponding frequency. For mid-high diagonal frequencies the values concentrate around 89%, while for horizontal and vertical frequencies it is equal to 11%, indicating that the former frequencies are more discriminative.\n\nTo further compare to the established frequency domain research, we extracted mid-frequency and high-frequency content, which is mostly used in related work for discrimination [A,D]. In such regions the distance values concentrate around 55% and 68%, respectively. As a conclusion, the difference between real and fake videos is more pronounced at mid-high diagonal frequencies.\n\n**Q8: Writing and organization.**\n\nWe will improve the algorithmic description of our method; add all suggested papers in the Related Work Section and change the order of the results.\n\n**Q9: Deepfake-eval-2024 dataset.**\n\nWe ran our detector on the fully synthetic videos, obtaining an AUC of 82.4%.\n\n**Q10: Size of training/validation sets are significantly low.**\n\nThis is true, however we do not see this as a weakness since our augmentation helps to compensate for that (this is also confirmed by the experiment we conducted in response to Q3)."}, {"review_id": "XlVPXEpwqK", "reviewer": "Reviewer_BuLg", "summary": "Recently, with the rapid development of video generative models, forensic detectors have also emerged; however, many of them lack generalizability. To address this issue, the authors propose a method that first identifies discriminative features that are less biased toward forensic-irrelevant patterns and more robust for detecting generated videos. Based on frequency domain analysis and previous literature, the authors identify that mid-high frequency components are less susceptible to compression artifacts and are thus suitable for forensic detection. To utilize these cues, two novel augmentation strategies are introduced during training: (1) Injection of forensic cues, by reconstructing real videos using the same autoencoder that generated the fake videos, aligning both real and fake samples in the representation space, and (2) Wavelet-based augmentation, by mixing real and fake videos using wavelet decomposition to enhance the mid-high frequency learning and push the detector to focus on those features. In the experimental evaluation, the model was trained using videos generated by the Pyramid Flow model, and testing was performed on both the GenVideo dataset (publicly available) and a newly constructed dataset comprising 2,400 videos from recent generative models. Accuracy was used as the evaluation metric, and the proposed method outperformed recent forensic detection methods.", "questions": "a. In lines 186–193, the authors state that replacing the low-frequency bands with those from the real counterpart forces the detector to focus on mid-high frequency features. However, how can we confidently claim that the detector will focus on mid-high frequencies? It is possible that the detector still learns from the low-frequency content provided by the real counterpart, potentially leading to misleading or incorrect detection. Do the authors have empirical evidence or observations supporting this assumption?\nb. The proposed method appears to heavily rely on the reconstruction quality of the Pyramid Flow model. While the paper mentions that this model generally reconstructs videos without visible artifacts, what happens if it fails in certain cases? Would such failure compromise the robustness or generalizability of the detector trained using this reconstruction-based augmentation?\nc. The paper makes valuable contributions, including a new dataset and a promising method. However, neither the code nor the dataset has been made available. The authors are strongly encouraged to release both the code and dataset to enable transparent evaluation, reproducibility, and faster progress in the rapidly evolving field of forensic video detection.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "1. Quality: \nThe submission is technically sound, presenting a relevant formulation and effective use of forensic feature extraction from videos. The claims are well-supported by experimental results. The methods employed are appropriate, and the work is presented as a complete and cohesive study. The authors are transparent about both the strengths and limitations of their approach.\n\n2. Clarity:\nThe submission is clearly written and easy to follow.\n\n3. Significance:\nThe results are impactful for the community. While the code and new dataset have not been released, if shared, this work has strong potential to be used by other researchers for further development and benchmarking.\n\n4. Originality:\nThe work integrates several ideas from previous methods, with appropriate citations. Although the application of these ideas to video forensics is a novel context, the core techniques themselves are adapted rather than newly proposed. Therefore, the method is not entirely novel in its formulation.", "was_revised": true, "final_justification": "Thank you to the authors for the clarification on all the raised questions.", "rebuttal": "We thank the reviewer for their positive feedback on our paper. We are glad that they found our “submission technically sound”, the “claims well-supported by experimental results”, our “submission clearly written and easy to follow” and that our “results are impactful for the community”.\n\nIn the following, we address each question individually.\n\n**Q1: The paper makes valuable contributions, including a new dataset and a promising method. However, neither the code nor the dataset has been made available. The authors are strongly encouraged to release both the code and dataset to enable transparent evaluation, reproducibility, and faster progress in the rapidly evolving field of forensic video detection.**\n\nWe plan to publicly release both the dataset that we created with new synthetic generators and the code of our method to guarantee reproducibility, and to help advance the research field on video forensics.\n\n**Q2: a. In lines 186–193, the authors state that replacing the low-frequency bands with those from the real counterpart forces the detector to focus on mid-high frequency features. However, how can we confidently claim that the detector will focus on mid-high frequencies? It is possible that the detector still learns from the low-frequency content provided by the real counterpart, potentially leading to misleading or incorrect detection. Do the authors have empirical evidence or observations supporting this assumption?**\n\nTo gain better insights into this issue we conducted the following additional experiment: at test time we modified the real videos and injected the mid-high frequencies of fully synthetic videos into them. The results of our detector with this test set are presented below. We observe that the results in terms of AUC and balanced accuracy (bAcc) get worse (second row). Moreover, in terms of the True Negative Rate (TNR), which is the probability that real videos are correctly classified as real, it goes from 98.7 to 0.2 (on average), which clearly shows that the detector inverts its decision. This highlights the importance of mid-high frequencies to establish if a video is fake or not.\n\n| AUC / bAcc |  Allegro    |  Cogvideo X |  Mochi-1    | OpenSora-Pl.|     AVG     |\n|:-----------|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|\n|            | 98.6 / 91.5 | 97.2 / 85.0 | 99.1 / 93.5 | 99.8 / 97.0 | 98.7 / 91.8 |\n| **         | 21.8 / 42.2 | 13.0 / 36.0 | 20.2 / 44.2 | 40.9 / 47.7 | 24.0 / 42.5 |\n\n**real videos with mid-high frequencies of fully-generated videos\n\n**Q3: The proposed method appears to heavily rely on the reconstruction quality of the Pyramid Flow model. While the paper mentions that this model generally reconstructs videos without visible artifacts, what happens if it fails in certain cases? Would such failure compromise the robustness or generalizability of the detector trained using this reconstruction-based augmentation?**\n\nEven though our method is trained on high-quality synthetic videos generated by the Pyramid Flow model (with a quality score above 84% on the VBench Leaderboard [30]), it is still able to detect videos with visible artifacts. This is evidenced by the fact that for videos generated by the ModelScope model that exhibit visible artifacts (with a quality score below 76% on the same benchmark [30]), our method achieves a high accuracy of 97.1%. Furthermore, overall, from our experiments we were not able to observe a different behaviour between older and newer synthetic generators (the former containing more visible artifacts) (see Table 3 and 4 of our paper). Our conjecture is that the detector is mostly focused on low level traces and is not very much influenced by semantic inconsistencies."}, {"review_id": "rWymwuXjeS", "reviewer": "Reviewer_5ZLP", "summary": "This paper addresses the critical challenge of generalizing AI-generated video detection by proposing a novel forensic-oriented data augmentation strategy. Recognizing that existing detectors often struggle due to reliance on high-level, generator-specific semantic artifacts, the authors shift the focus towards intrinsic low-level, frequency-domain traces that are consistent across various generative architectures. Specifically, the paper introduces a wavelet-based augmentation strategy that selectively emphasizes mid-high diagonal frequency components robust to video compression artifacts. The proposed method is evaluated using videos from multiple generative models, achieving substantial improvements over current state-of-the-art methods, especially in cross-generator generalization scenarios.", "questions": "1.Temporal Artifacts: Given the promising results, how could the proposed augmentation strategy be extended or adapted to explicitly include temporal forensic artifacts, which could further enhance detection performance?\n2.Future Generators: How robust do you expect your method to be against significantly different generative paradigms that may emerge in the future? What adaptations, if any, would be necessary to maintain high generalization?\n3.Computational Efficiency: While wavelet decomposition is mentioned as computationally efficient, could you provide specific benchmarking or computational complexity analyses comparing this augmentation strategy to simpler augmentations such as MixUp or CutMix?\n4.Dataset Diversity: Do you anticipate similar performance benefits when applying your augmentation strategy to significantly smaller or differently structured training datasets?", "limitations": "Yes, the authors clearly and effectively addressed limitations and potential negative societal impacts. They acknowledge explicitly the method's limitation in focusing primarily on spatial artifacts and highlight the potential need for adaptation to new generative paradigms.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\nQuality:\n1.The methodology is rigorous, clearly motivated by a thorough analysis of the frequency-domain forensic artifacts inherent in synthetic video generation.\n2.Experiments are comprehensive and well-structured, using diverse datasets including recent generators and established benchmarks.\n3.Metrics used (AUC, balanced accuracy, Pd@5%, NLL, ECE) are relevant and provide a nuanced evaluation.\nClarity:\n1.The paper is very clearly written, with logical progression from motivation through method, results, and discussion.\n2.Figures effectively illustrate the key insights about forensic artifacts and the proposed augmentation approach.\nSignificance:\n1.The approach directly addresses a significant limitation in current forensic video detection research, offering practical strategies for real-world applicability.\n2.Results demonstrate meaningful improvements in generalization, a critical aspect given the rapidly evolving nature of generative technologies.\nOriginality:\n1.The wavelet-based augmentation strategy is novel within the context of forensic-oriented video detection.\n2.The explicit focus on intrinsic, compression-resistant frequency bands is a valuable innovation in the forensic detection literature.\n\n\nWeaknesses:\n1.The method primarily focuses on spatial frequency components, explicitly noting a limitation in addressing temporal artifacts, which could be important in video detection tasks.\n2.While the approach improves generalization substantially, the robustness of this augmentation strategy to drastically different future generative paradigms remains somewhat uncertain.", "was_revised": true, "final_justification": "Tthe authors responded to all the comments, the overall quality of the paper is good.", "rebuttal": "We thank the reviewer for their positive feedback on our paper. We are glad that they found our methodology “rigorous, clearly motivated by a thorough analysis of the forensic artifacts”, our wavelet-based augmentation strategy “novel within the context of forensic-oriented video detection”, our experiments “comprehensive and well-structured, including recent generators and established benchmarks”, our paper “clearly written, with logical progression from motivation through method, results, and discussion”, that our “approach directly addresses a significant limitation in current forensic video detection research” and that our “results demonstrate meaningful improvements in generalization”.\n\nIn the following, we address each question individually.\n\n**Q1: Temporal Artifacts: Given the promising results, how could the proposed augmentation strategy be extended or adapted to explicitly include temporal forensic artifacts, which could further enhance detection performance?**\n\nOur augmentation strategy is designed to prevent the detector from focusing on horizontal and vertical spatial frequencies where compression artifacts may overlap with forensic cues. Similarly, this approach can be extended to the temporal domain, encouraging the detector to exploit temporal forensic artifacts. One possible direction is to replace the low temporal frequencies of the fake video with those from the real video during training, while preserving the mid-to-high temporal frequencies of the fake content. We plan to take this direction in our future work as well as better understand the forensic artifacts in the temporal domain, since we believe that the replacement strategy should be focused on specific temporal frequencies. However, a key challenge in this respect is to identify a 3D backbone visual encoder capable of matching the performance of 2D models like DINOv2. As we can see from Table 2 the performance of the latter is significantly better than that of the 3D Hiera video model.\n\n**Q2: Future Generators: How robust do you expect your method to be against significantly different generative paradigms that may emerge in the future? What adaptations, if any, would be necessary to maintain high generalization?**\n\nThe forensic artifacts that we have analyzed in our work are caused by the upsampling operation in the network’s  architecture (resulting in peaks in the frequency domain), which has also been shown in prior work [A,B]. This is caused by the inability of the generator to perfectly reproduce mid frequencies of the video signal as also shown in [C,D]. If new generative paradigms still include upsampling, then such traces will likely be present (maybe only in different positions). This is also what we observed with autoregressive models that are different from diffusion models. Despite having a different generative paradigm, they still present similar artifacts (see Figure 2 of the main paper). However, it is not easy to predict what will happen in the future. Hence if the generative paradigm will completely change, then the method would probably require re-training on a more recent generator. Even so, we believe that the discussion on the compression artifacts would still be relevant and hence our augmentation strategy could still be applied as is.\n\nReferences:  \n[A] Zhang et al. Detecting and Simulating Artifacts in GAN Fake Images, WIFS 2019.  \n[B] Vahdati et al. Beyond deepfake images: Detecting AI-generated videos, CVPRW 2024.  \n[C] Dzanic et al. Fourier spectrum discrepancies in deep network generated images, NeurIPS 2020.  \n[D] Corvi et al. Intriguing properties of synthetic images: from generative adversarial networks to diffusion models, CVPRW 2023.  \n\n**Q3: Computational Efficiency: While wavelet decomposition is mentioned as computationally efficient, could you provide specific benchmarking or computational complexity analyses comparing this augmentation strategy to simpler augmentations such as MixUp or CutMix?**\n\nWhile wavelet decomposition is computationally efficient, it is slower than operations such as MixUp or CutMix. More specifically, the wavelet augmentation (with three decomposition levels) requires about 21*N FLOPs (floating-point operations), while MixUp requires only 3*N FLOPs, where N is the number of pixels. However, the implementation of the wavelet decomposition using convolution reduces the execution time substantially on parallel hardware architectures (GPUs) with an overall execution time of 18.6 ms for batch compared to 5.2 ms for MixUp on a A6000 Nvidia Quadro. This increase in execution training time is compensated, however, by an improvement in terms of accuracy (+3% and +5%, Table 1).\n\n**Q4: Dataset Diversity: Do you anticipate similar performance benefits when applying your augmentation strategy to significantly smaller or differently structured training datasets?**\n\nWe conducted two additional experiments: (A) we used only 20% of our training set and (B) we trained on videos generated with a different synthetic generator (CogVideoX). Results for both experiments are presented below. In the first case we observe that our proposed augmentation strategy is still able to provide an advantage on a significantly smaller training set (+15% in terms of balanced accuracy) (second row).\n\nTable A: 20% of training set from Pyramid flow.\n| AUC / bAcc   |  Allegro    |  Cogvideo X |  Mochi-1    | OpenSora-Pl.|     AVG     |\n|:-------------|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| without aug. | 94.3 / 70.8 | 89.2 / 62.8 | 93.9 / 77.8 | 98.4 / 87.3 | 94.0 / 74.7 |\n| with aug.    | 97.6 / 88.3 | 95.2 / 82.8 | 98.8 / 92.7 | 99.8 / 96.5 | 97.8 / 90.1 |\n\nIn the second experiment we used CogvideoX, instead of Pyramid Flow, during training. In this case too, we observe a significant advantage of the proposed augmentation strategy (+5% in terms of AUC and +13% in terms of balanced accuracy) (second row).\n\nTable B: Using CogVideoX as the training set.\n| AUC / bAcc   |  Allegro    | Pyramid Flow|  Mochi-1    | OpenSora-Pl.|     AVG     |\n|:-------------|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| without aug. | 90.7 / 69.3 | 98.0 / 87.3 | 90.7 / 72.0 | 94.9 / 78.0 | 93.6 / 76.7 |\n| with aug.    | 98.5 / 88.8 | 99.4 / 93.8 | 97.6 / 85.2 | 99.2 / 92.8 | 98.7 / 90.2 |"}, {"review_id": "kUz1klbVhh", "reviewer": "Reviewer_Nt42", "summary": "The submission proposes a combination of two data augmentation strategies relying on a very recent video encoder (Pyramid Flow, ICLR 2025) for the detection of ai-generated videos.\n\nThe first data augmentation strategy (Section 3.2) relies on encoding real videos in an autoencoder fashion.\nThe second data augmentation strategy (Section 3.2, fig 4) runs a Haar transform on an AI-generated video, embeds for the low band the frequencies taken from real videos and inverts it to create an altered AI-generated video. This aims to force the method to look at mid to high frequencies.\n\nThey observe that several SOTA methods which perform well on videos from generators of 2022-2023 perform worse on videos from generators of 2024-now.\n\nThey show good performance for video detection with their method.  They show that retraining the other SOTA detectors does not close the gap to their proposed method fully.\n\nThey show that one image-based method improves substantially and gets close to the proposed method when retrained using the encoder from Pyramid Flow.\n\nMore Detailed technical descriptions for the implementation can be found in the supplement section D.", "questions": "- Can you place a link to section D of the appendix in the main paper in section 4 ?\n \n- A graphic with the full prediction model (incl link to the backbones discussed) in the supplement would be helpful .  That would help to consolidate the information which is a bit scattered in  the main paper.", "limitations": "They discussed the limitations meaningfully.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- easy to read\n- simple idea\n- training details in the supplement are available\n\n- ablation study that attempts to use the proposed encoder with other near-SOTA methods\n- ablation study on data augmentation\n- ablation study on backbones\n\nWeaknesses:\n- somewhat limited novelty", "was_revised": true, "final_justification": "Good paper, maybe an oral.", "rebuttal": "We thank the reviewer for their positive feedback. We are glad that they found our paper “easy to read”, with a “simple idea” and containing an “extensive ablation study”.\n\nIn the following, we address each question individually.\n\n**Q1: Can you place a link to section D of the appendix in the main paper in section 4?**\n\nThank you for the suggestion! Yes, we will include this link in the paper and better connect the main paper to the appendix.\n\n**Q2: A graphic with the full prediction model (incl link to the backbones discussed) in the supplement would be helpful. That would help to consolidate the information which is a bit scattered in the main paper.**\n\nYes, we will add such a graphic in the supplemental material and in the main paper, and describe more clearly the information about the models used."}]

Author rebuttal:

Meta-review: This manuscript addresses the task of generalizable AI-generated video detection. It proposes a forensics-oriented data augmentation strategy which is based on a wavelet decomposition, and augmenting particular frequency bands such as to encourage detectors to learn to recognize forensic traces that are consistent across various generative architectures and robust to video compression artifacts. 

Reviewers highlight the clear motivation of the approach and the promising results. All reviews agree that the paper should be accepted. The AC agrees, yet strongly recommends to discuss related work that reduces frequency artifacts in the generator right away as well as previous works discussing generalizable generated image detection beyond artifacts, for example:

Durall et al: Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions, CVPR 2020
He et al.: Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis, IJCAI 2021
Jung et al.: Spectral Distribution aware Image Generation, AAAI 2021
Decision: Accept (poster)