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REVIEW 2 major objections 6 minor 3 cited by

Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics

T0 review · 2 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read All 24 tested deepfake detectors lose accuracy on a new, more diverse benchmark.

desk verdict A genuinely large and method-diverse deepfake video benchmark that deserves a serious referee, but its central 'generalizable forensics' interpretation is undercut by a talking-face construction confound that the paper never ablates. read the letter →

arxiv 2507.18015 v1 pith:T56O23BW submitted 2025-07-24 cs.CV

Yuezun Li , Delong Zhu , Xinjie Cui , Siwei Lyu This is my paper
classification cs.CV
keywords deepfakedetectionbenchmarkgeneralizableforensicsface-swapface-reenactmenttalking-facevideoAUCevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces Celeb-DF++, a large-scale video deepfake benchmark built to test whether detectors can generalize to unseen forgery types. It expands the earlier Celeb-DF dataset with 53,196 fake videos generated by 22 recent methods spanning face-swap, face-reenactment, and talking-face scenarios. Evaluated on this benchmark, all 24 recent detection methods drop in AUC compared with Celeb-DF, with average falls of about 5.2 points at frame level and 7.1 points at video level. In a cross-method protocol, the detectors average roughly 72 percent AUC, indicating that none of them reliably identifies forgeries produced by methods they were not trained on. The paper argues that current detectors are not yet ready for generalizable forensics and offers the benchmark as a shared testbed to drive progress.

What carries the argument

The central object is the benchmark itself: 53,196 forged videos produced by 22 methods (8 face-swap, 7 face-reenactment, 7 talking-face), built on 590 real videos of 59 celebrities. The evaluation machinery is a set of three protocols: GF-eval trains detectors on Celeb-DF in the face-swap scenario and tests across all other methods and scenarios; GFQ-eval adds H.264 compression at two strengths; GFD-eval trains on FaceForensics++ (HQ) and tests across all Celeb-DF++ methods. These protocols convert the question 'can detectors generalize?' into measurable AUC scores at frame and video level.

What would settle it

A detector that generalizes well on Celeb-DF++ but fails on an independent collection of real-world deepfakes (or vice versa) would disconfirm the benchmark's role as a proxy for real-world difficulty. Concretely, one could train a detector on Celeb-DF++ face-swap methods and evaluate it on a held-out set of verified in-the-wild deepfake videos; if its AUC there is close to chance while its Celeb-DF++ AUC is high, the benchmark's cross-method scores overstate real-world generalizability.

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Extended reading notes

Core claim

The central discovery claimed in this paper is that the difficulty of deepfake detection is substantially underestimated when benchmarks contain only a small number of forgery methods. By extending Celeb-DF to include 22 generation pipelines across three scenarios, the authors show that every one of 24 detectors performs worse on Celeb-DF++ than on the original Celeb-DF, and that cross-method generalization remains low even for the latest detectors. The work further claims that compressed versions of the benchmark degrade performance further, and that detectors trained on FaceForensics++ lose additional accuracy under a cross-dataset protocol. The result is presented as evidence that forgery diversity, not just scale, is the key missing ingredient in existing benchmarks.

Load-bearing premise

The claim that the benchmark measures generalizable forensics rests on the assumption that the artifacts left by these 22 generation pipelines are representative of the deepfakes a detector would meet in the wild, so that a detector's failure on them predicts failure on real-world forgeries.

Editorial extensions

If this is right

  • State-of-the-art detectors overstate their real-world readiness: their published AUCs on single-method benchmarks do not transfer to diverse forgeries.
  • Cross-method generalization, not just within-method accuracy, becomes the primary evaluation target for new detectors.
  • Compression is a first-order obstacle: stronger H.264 compression (c45) cuts average frame-level AUC by an additional 4.4 points over already-low cross-method scores.
  • Training domain matters: detectors trained on FaceForensics++ do worse on Celeb-DF++ than detectors retrained on Celeb-DF, confirming a domain-gap penalty in GFD-eval.
  • The best-performing detector in the benchmark is not a solution: it leads within face-swap but degrades sharply on talking-face methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The talking-face videos are generated from a single first frame and five VoxCeleb2 audio clips, so their motion and background are simpler than in-the-wild talking-head videos; a detector that exploits this pipeline's constraints might score well on the benchmark while failing on real social-media forgeries.
  • The benchmark stops at 2024-generation methods; diffusion-based video generators that appeared later could behave differently, so the measured gap may be a floor or a ceiling for future detectors.
  • A productive next test would be to use the benchmark to measure per-method difficulty and correlate it with model architecture, which would tell practitioners which kinds of forgery artifacts are the hardest to model.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper introduces Celeb-DF++, a large-scale video deepfake benchmark extending the authors' earlier Celeb-DF dataset. It contains 53,196 (sic) fake videos generated by 22 methods spanning face-swap (FS), face-reenactment (FR), and talking-face (TF) scenarios, and evaluates 24 detection methods under three new protocols: GF-eval (cross-method generalization), GFQ-eval (cross-compression robustness), and GFD-eval (cross-dataset generalization). The reported results show that all assessed detectors drop on Celeb-DF++ relative to Celeb-DF, with average decreases of approximately 5.2% frame-level AUC and 7.1% video-level AUC, and that the eight detectors retrained under GF-eval average about 71.7% frame-level and 72.1% video-level AUC. The dataset is released publicly.

Significance. If the construct-validity issues are addressed, this is a potentially valuable community resource: it is one of the largest and most method-diverse video deepfake benchmarks, with 22 generation pipelines and 24 assessed detectors, and the proposed per-method evaluation tables are detailed enough to support future comparisons. The authors are transparent about protocol details and release the dataset, which will enable reproducibility. However, the benchmark's interpretation as a faithful measure of generalizable forensics for real-world forgeries is not yet secured: the TF construction protocol and the GFQ-eval compression design may confound the measured difficulty with artifacts of the synthetic pipeline. Since the paper's central claim is that the benchmark reflects the diversity of forgeries 'witnessed in the wild,' the current evidence is incomplete without human evaluation, quality metrics, or ablations that separate method-specific artifacts from shared construction choices.

major comments (2)
  1. [III-B, Tables VI-VII] The Talking-face scenario is constructed from the first frame of each real video as a single source image, driven by audio segments from VoxCeleb2 that do not match the target speaker (Section III-B, Data Generation Details). Because all seven TF generators share this protocol, the uniformly low per-method AUCs in the TF columns of Tables VI and VII (e.g., EchoMimic 51.2/46.3, SadTalker 53.8/45.7) may be driven by these common pipeline artifacts—static background, frozen identity image, audio-visual mismatch—rather than by the subtlety of the manipulations or by genuine diversity of real-world talking-face forgeries. The manuscript provides no human realism evaluation, no per-method quality or failure-rate statistics, and no ablation separating method-specific artifacts from the shared source-frame/audio protocol. As the paper's stated contribution is a benchmark for generalizable forensics against forgeries 'witnessed in the wild,' this construct-validity gap leaves the central interpretation of the cross-scenario drops unsecured. Please add a limitations discussion and supporting analyses (e.g., full-video sources, matched audio, or human perceptual ratings).
  2. [III-E, Protocol #2] In the GFQ-eval protocol, the text states that FFmpeg is used to compress 'DeepFake videos,' and the table captions refer to compression of the fake methods; there is no statement that the real videos are subjected to the same H.264 (c35/c45) re-encoding. If the real test videos retain their original quality while only the forged videos are re-compressed, the AUC drops in Tables VIII–XI conflate the obscuration of forgery traces with a class-conditional quality artifact that detectors can exploit (e.g., recognizing H.264-induced artifacts as a fake cue). Please clarify whether real videos are compressed in the same way; if they are not, the protocol should be re-run with matched compression of both classes, or the paper should explicitly justify why the current procedure is a valid measure of compression robustness.
minor comments (6)
  1. [III-B] The phrase 'randomly select 5 audio segments from VoxCeleb2 for each video frame' is ambiguous and likely meant 'for each video' rather than 'for each video frame.' Please clarify the audio-selection procedure.
  2. [III-B, Abstract] The total fake-video count of 53,196 does not match the sum of the per-method counts shown in Fig. 2 (5,639 + 13,646 + 13,631 + 20,279 = 53,195). In addition, the sentence 'For Face-swap and Face-reenactment scenario, we randomly select 2,000 identity pairs ... resulting in 13,646 videos' misattributes the FS-only count to both scenarios; the seven additional FS methods sum to 13,646, while the seven FR methods sum to 13,631. Please correct the counts and the wording.
  3. [III-C] The statement that all detectors are run 'using their default settings' is not fully specified; it is unclear whether the default settings in DeepfakeBench match the original papers' recommended hyperparameters, and whether the recent methods (CFM, ProDet, ForAda, Effort) are trained with identical data splits. Please provide implementation details or a pointer to the code repository.
  4. [III-E, GF-eval] The selection of eight detectors for retraining under GF-eval should be justified; please specify which of the 24 detectors had released training code and how the chosen eight are representative of the design-space categories in Table III.
  5. [Section III-D] The reported average decreases of 'approximately 5.2% in frame-level AUC and 7.1% in video-level AUC' do not specify the baseline version of Celeb-DF; from Tables IV and V, the drops are relative to the v2 (official) version. Please state this explicitly.
  6. [Throughout] There are several minor typos and grammatical issues, including 'developement' (Introduction), 'larges-scale' (Related Works), 'boarder range' (Conclusion), and inconsistent use of singular/plural in 'Face-swap and Face-reenactment scenario.' A careful proofread is recommended.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical benchmark measurement, not a derivation whose outputs are equivalent to its inputs.

full rationale

Celeb-DF++ is an empirical measurement paper. Its central results, such as the 'average decrease of approximately 5.2% in frame-level AUC and 7.1% in video-level AUC' (Section III-D) and the GF-eval averages of 71.7% / 72.1% (Tables VI and VII), are obtained by training detectors on FF++ (HQ) or Celeb-DF and testing them on newly generated videos; no parameter is fitted to the Celeb-DF++ test data and then renamed as a prediction. The drop in AUC is a measured outcome across 24 detectors, not a quantity that is equal to the dataset construction by definition. The paper's self-references — Celeb-DF [21] as the base dataset, DeepfakeBench [94] as the implementation infrastructure, and ForAda [15] as one of the 24 assessed detectors — are provenance and evaluation constituents rather than load-bearing unverified premises that the conclusion reduces to. The skeptical concern that the talking-face videos are synthesized from a single first frame with VoxCeleb2 audio is a construct-validity question about whether the measured drop reflects real-world forgery difficulty; it does not make any prediction equivalent to its input. No circular step satisfying the required reduction standard can be exhibited, so the score is 0.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

No fitted scientific parameters: this is an empirical benchmark. The listed free parameters are hand-chosen evaluation constants on which the headline AUC drops directly depend. The axioms are representativeness and fairness assumptions shared by benchmark papers in this line; the strongest is the pipeline-representativeness assumption for the talking-face scenario. No new theoretical entities are introduced.

free parameters (3)
  • Number of test videos per method (200/200/300)
    Hand-chosen test-set sizes per scenario (Section III-B); they set the variance of all reported AUCs, but no variance is reported.
  • Compression levels c35 and c45 = H.264 CRF 35 and 45
    Hand-selected to represent medium and high compression in GFQ-eval (Section III-E, Protocol #2); the measured 3.5%, 4.4%, 2.2%, and 7.5% drops are defined relative to these two arbitrary levels.
  • 32 sampled frames per video = 32
    Sampling count for frame-level and video-level AUC (Section III-C); a standard but hand-chosen evaluation constant.
assumptions (3)
  • domain assumption The 22 selected generation methods represent the space of real-world deepfakes ('covering the most prevalent DeepFake cases witnessed in the wild').
    Abstract and Section I; no prevalence data or independent survey supports the representativeness claim, and no overlap analysis versus methods already in DF-Platter or DefakeAVMiT is given.
  • domain assumption Detectors run with default settings and DeepfakeBench pipelines represent each detector's intended capability.
    Section III-C; if defaults are suboptimal for specific generators, the benchmark understates detector performance.
  • domain assumption Frame-level and video-level AUC computed on 32 sampled frames captures detection performance faithfully.
    Section III-C; standard for the field, but video-level pooling over 32 frames is an arbitrary aggregation.

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Cite this review

Pith. "Pith review of Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics." pith.science (2026). https://pith.science/paper/T56O23BW

@misc{pith2026250718015,
  author       = {Pith},
  title        = {Pith review of: Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T56O23BW}},
  note         = {Machine review of arXiv:2507.18015}
}
read the original abstract

The rapid advancement of AI technologies has significantly increased the diversity of DeepFake videos circulating online, posing a pressing challenge for \textit{generalizable forensics}, \ie, detecting a wide range of unseen DeepFake types using a single model. Addressing this challenge requires datasets that are not only large-scale but also rich in forgery diversity. However, most existing datasets, despite their scale, include only a limited variety of forgery types, making them insufficient for developing generalizable detection methods. Therefore, we build upon our earlier Celeb-DF dataset and introduce {Celeb-DF++}, a new large-scale and challenging video DeepFake benchmark dedicated to the generalizable forensics challenge. Celeb-DF++ covers three commonly encountered forgery scenarios: Face-swap (FS), Face-reenactment (FR), and Talking-face (TF). Each scenario contains a substantial number of high-quality forged videos, generated using a total of 22 various recent DeepFake methods. These methods differ in terms of architectures, generation pipelines, and targeted facial regions, covering the most prevalent DeepFake cases witnessed in the wild. We also introduce evaluation protocols for measuring the generalizability of 24 recent detection methods, highlighting the limitations of existing detection methods and the difficulty of our new dataset.

Figures

Figures reproduced from arXiv: 2507.18015 by the authors.

Figure 1
Figure 1. Overview of the proposed Celeb-DF++ benchmark. This benchmark is motivated by the need of generalizable forensics in deepfake detection. their diversity. With continuous architectural refinements and evolving generation strategies, DeepFakes become increas￾ingly diverse, and more concerningly, many of their details are likely unknown. This circumstance raises a practical and urgent challenge for DeepFake detection, … view at source ↗
Figure 2
Figure 2. The sunburst chart highlights that our Celeb-DF++ includes a wide range of both DeepFake methods and assessed detection methods. See Table I, Table II and Table III for details. the target individuals. For each scenario, we adopt a diverse set of state-of-the-art DeepFake methods, 8 for FS, 7 for FR, and 7 for TF, covering 22 representative methods in total. These methods vary in architectures, generation pipelines,… view at source ↗
Figure 3
Figure 3. Visual examples of Celeb-DF++. The first column denotes the source identity face and the bottom-left embedded image represents the target face or the driving face/audio. The remaining columns display the results generated by each DeepFake method. TABLE III ASSESSED DEEPFAKE DETECTION METHODS. Detector Venue Architecture Category5 Code MesoNet [4] WIFS’18 Designed CNN Data-driven Link MesoInception [4] WIFS’18 Design… view at source ↗

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Uncertainty-Aware Deepfake Detection via Multi-View Structural Learning

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  2. Continuously Evolving Deepfake Detection: An Architecture and Public-Benchmark Evaluation of a Dynamic Detection System

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    A continuously refreshed, incentive-driven deepfake detector beats static detectors on in-the-wild benchmarks and improves on post-export AI-generated media.

  3. Physiological Signals as a Forensic Modality for Talking-Face Deepfake Detection

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    A classifier trained on rPPG waveforms from face videos detects talking-face deepfakes with AUC 0.806, and detection difficulty varies by generator (AUC 0.690–0.985).

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.