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AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper introduces AV-Deepfake1M++, a two-million-clip audio-visual deepfake benchmark combining nine generation models and 36 real-world perturbations, and reports that prior temporal localization methods collapse on it.

desk verdict Large, genuinely useful deepfake benchmark, but the missing quality control on the generation pipeline is the load-bearing issue. read the letter →

arxiv 2507.20579 v1 pith:HYDZ4M2N submitted 2025-07-28 cs.CV

Zhixi Cai , Kartik Kuckreja , Shreya Ghosh , Akanksha Chuchra , Muhammad Haris Khan , Usman Tariq , Tom Gedeon , Abhinav Dhall This is my paper · ORCID
classification cs.CV
keywords audio-visualdeepfaketemporalforgerylocalizationbenchmarkdatasetreal-worldperturbationstext-to-speechlip-syncgenerationLLM-drivensemanticmanipulationdetection
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 AV-Deepfake1M++, a benchmark of just over two million audio-visual video clips, about 4,656 hours, built from three real-video sources and designed for training and testing deepfake detectors under conditions closer to online distribution. It extends an earlier dataset by adding two more source datasets, nine generation models spanning speech synthesis and lip-sync, and 36 audio and visual perturbations such as compression, noise, frame jitter, and reverberation. The dataset carries frame-level and video-level annotations for both binary classification and temporal forgery localization, with held-out test sets whose identities, sources, generation methods, and perturbation schedules differ from training. Challenge results reported in the paper show strong classification performance near 0.98 AUC, while a previous state-of-the-art temporal localization method falls from 96.30 to 14.71 AP@0.5, illustrating the difficulty the new perturbations introduce.

What carries the argument

The load-bearing machinery is the automated generation pipeline: an LLM plans word-level edits, text-to-speech models synthesize the manipulated audio, lip-sync models re-render the mouth region, and post-processing assembles edited segments into untouched video and applies audio and visual perturbations. The four manipulation types separate audio and visual contributions, and the split design isolates perturbations between training and test so that testA and testB measure cross-domain generalization. Dataset statistics—2,051,154 clips, 7,109 subjects, 4,655.9 hours—and a held-out identity/source/generator separation carry the claim that this is a large, diverse benchmark rather than a simple collection of fakes.

What would settle it

Take a random sample of, say, 1,000 clips from the fake split of AV-Deepfake1M++, and have human annotators or an independently trained audio-visual synchronization model verify that each clip actually contains the intended semantic change and that the audio and visual manipulations are aligned in time. If a non-trivial fraction of clips are mislabeled or misaligned, the benchmark's labels and the leaderboard numbers derived from them would need to be recomputed.

Watch

Extended reading notes

Core claim

The central claim is that a deepfake benchmark needs scale, generation diversity, and real-world perturbations together, and that AV-Deepfake1M++ provides all three. Real clips come from VoxCeleb2, LRS3, and EngageNet, covering interviews, TED talks, and conversational meetings. Fake clips are produced by an LLM that plans token-level insert, replace, and delete edits to invert the meaning of spoken sentences, followed by one of four text-to-speech models for speech and one of three lip-sync models for video, yielding four manipulation types: real, fake audio with real video, real audio with fake video, and fully fake. After assembly, clips may receive zero to several of 36 perturbations. On this material the best challenge entry reaches 97.83 AUC on testA classification, while the strongest prior temporal-localization baseline collapses to 14.71 AP@0.5, which the paper attributes to the new perturbations and unseen synthesis pipelines.

Load-bearing premise

The automatic generation pipeline assumes every assembled clip is correctly labeled; if the language model's semantic edit, the synthesized speech, or the lip-sync alignment fails, the clip's real/fake label and segment boundaries are wrong and every benchmark score inherits that error.

Editorial extensions

If this is right

  • Detectors trained on AV-Deepfake1M++ must face unseen generation methods and perturbation schedules in testA and testB, so leaderboard scores reflect cross-domain robustness rather than in-domain memorization.
  • The reported collapse of BA-TFD+ from 96.30 AP@0.5 on LAV-DF to 14.71 AP@0.5 on AV-Deepfake1M++ indicates that perturbation diversity can invalidate methods tuned to artifacts of a single generator.
  • The four manipulation types and frame-level annotations allow separate evaluation of audio-only, video-only, and fully cross-modal forgery detection and localization.
  • With best classification AUC at 97.83 but top localization score near 67, classification appears close to saturated while temporal localization remains the open problem.

Reading between the lines

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

  • Because every clip is labeled by an automatic pipeline with no reported verification step, a human audit of a random sample would be the natural check on label quality; all benchmark numbers inherit that pipeline's error rate until then.
  • Since perturbations are separated between training and test sets, test scores measure a mixture of forgery detection and robustness to distribution shift; a detector that keys on perturbation artifacts rather than semantic manipulation could still score well without truly understanding forgeries.
  • An extension the paper does not test is whether training on AV-Deepfake1M++ transfers to other benchmarks such as FaceForensics++ or DFDC; a positive transfer result would strengthen the claim that the dataset captures general forgery cues.
  • The choice of LLM-planned meaning inversion points to a future in which fakes act at the level of content semantics, not low-level pixels or waveforms, which would push detection toward checking audio-visual semantic consistency across modalities.
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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

3 major / 5 minor

Summary. The paper introduces AV-Deepfake1M++, a large-scale audio-visual deepfake benchmark containing 2,051,154 clips (about 4,655.9 hours) built from VoxCeleb2, LRS3, and EngageNet. The generation pipeline uses an LLM to plan token-level insert/replace/delete edits, TTS models to synthesize modified speech, and lip-sync models to synthesize modified video; 36 audio-visual perturbations are then applied. The paper defines train/validation/testA/testB splits, reports dataset statistics, and benchmarks classification and temporal-localization methods, including results from the 2025 1M-Deepfakes Detection Challenge.

Significance. If the labels and manipulated-segment boundaries are correct, AV-Deepfake1M++ is a substantial community resource: it roughly doubles the scale of AV-Deepfake1M, adds source diversity, expands generation and perturbation diversity, and provides externally evaluated held-out test sets. The arithmetic in Table 3 is internally consistent, and the use of an independent challenge leaderboard is a practical strength. The main open question is whether the fully automatic generation pipeline produces reliable ground truth, since the paper does not report any verification or failure-rate analysis.

major comments (3)
  1. [Section 3.2, Tables 4-6] The dataset labels clips by provenance: every clip that passes through the pipeline is 'fake', and temporal localization annotations are inherited from the LLM's token-level plan without verification. The paper reports no quality-control step and no failure-rate measurement for the TTS, lip-sync, or assembly stages. If an LLM edit is not realized, if TTS duration drifts, or if lip-sync output is misaligned, the fake/real label or the segment boundary is wrong, and every AUC/AP/AR in Tables 4-6 is affected. Please add a validation protocol, such as ASR transcript verification, SyncNet consistency scoring, a human sample study, and per-pipeline failure rates, and report the results.
  2. [Section 1 and Table 2] The contribution bullet states that the pipeline includes 'nine state-of-the-art models' but Section 3.2 and Table 2 enumerate only seven (TalkLip, LatentSync, Diff2Lip; VITS, F5TTS, XTTSv2, YourTTS). The introduction also names 'visual-LipSync' and 'V ALLEX', which do not appear in Table 2. Please correct the count or list all models, and remove the four blank placeholder rows from Table 2; as written, the headline diversity claim is not auditable.
  3. [Section 4.3 and Table 2] The abstract and introduction claim '15 video-level and 11 audio-level distortions', but Table 2 lists 21 visual and 15 audio perturbation methods (36 unique rows, with PAD_SIMULATION duplicated). Please clarify whether the counts refer to a subset of perturbations, remove duplicate rows, and align the text with the table. The '36 perturbations' claim is a central contribution and must be precisely documented.
minor comments (5)
  1. [Section 5.1, Tables 5-6] The 'Score' column in Tables 5 and 6 is not defined; specify how the averaged AP and AR values are combined.
  2. [Table 4] TestB AUC is reported only for the Xception baseline; state whether the challenge teams were evaluated on TestB and, if so, provide the results.
  3. [Section 5.2] The sentence 'Pindrop Labs surpasses BA-TFD+ by ≈ 0.52 of the localization score' is ambiguous; the actual difference is 52.49 points (67.20 vs 14.71).
  4. [Section 3.1] The claim that test videos are 'encoded with various codecs' is not supported by any details; list the codecs and encoding parameters or remove the claim.
  5. [General] There are several language and formatting issues (e.g., 'we require datasets that rich in type' in the abstract, and the broken running title 'A V-Deepfake1M++'); a careful copyedit is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark is constructed from external corpora and generators and evaluated on held-out splits by independent challenge teams.

full rationale

AV-Deepfake1M++ is a dataset-construction and empirical-benchmarking paper rather than a derivation of a predicted quantity from fitted inputs. Its central claims—scale (2,051,154 clips), source diversity (VoxCeleb2, LRS3, EngageNet), generation diversity (multiple TTS and lip-sync models), and perturbation coverage—are supported by direct counts, by the described pipeline, and by held-out evaluations reported from external challenge teams (e.g., XJTU SunFlower Lab, Pindrop Labs). The forgery-generation pipeline in Section 3.2 is operational: an LLM plans token-level operations, Demucs separates speech from background noise, TTS and lip-sync pools synthesize content, and post-processing assembles the clip. Fake labels are assigned by provenance from that generation process; this is the standard definitional labeling of a synthetic benchmark, not a fitted prediction. The paper's self-citations to AV-Deepfake1M [3], LAV-DF [5], and BA-TFD+ [4] serve as pipeline ancestry and baseline methods, but they do not carry the load of the new benchmark's claims; the scores are measured on held-out TestA/TestB, and the best results come from non-author teams. The main weakness noted externally—absence of a stated quality-control or verification step for LLM/TTS/lip-sync outputs—is a genuine correctness and documentation risk for label reliability, but it is not circularity: any misalignment or failed edit would be an implementation defect, not a logical reduction of the dataset claim to its own inputs. Internal inconsistencies such as the 'nine' generation models in Section 1 versus the seven enumerated in Section 3.2 and Table 2, and the blank rows in Table 2, are documentation issues and do not constitute circular steps.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim is a scale and diversity benchmark claim. It rests on the trustworthiness of the public source corpora, the correctness of the automatic generation pipeline, and the realism of synthetic perturbations. No free parameters are fitted to data in the usual sense, because the dataset statistics are the result of the construction protocol rather than a fitted model.

assumptions (4)
  • domain assumption VoxCeleb2, LRS3, and EngageNet videos are unmanipulated and correctly labeled real.
    Section 3.1 retrieves original videos from these public datasets; if any contain deepfakes or mislabeled content, the 'real' set is polluted and all comparisons are affected.
  • domain assumption The LLM-generated edit plans and the TTS and lip-sync outputs faithfully implement the intended semantic manipulations.
    Section 3.2 uses GPT-4o-mini and GPT-3.5-turbo to plan replace, insert, and delete operations and synthetic models to generate speech and video, with no stated verification step, so fake labels depend on pipeline correctness.
  • domain assumption The 36 perturbations are representative of real-world streaming and redistribution artifacts.
    Section 3.3 lists Gaussian noise, compression, frame drops, jitter, reverb, and similar effects, but provides no measurement or comparison to actual online video distribution, so realism is assumed.
  • domain assumption The benchmark splits and metrics measure cross-domain generalization as claimed.
    Section 3.4 splits TestA and TestB on different identities, sources, and generation methods, but test perturbations are also different by design, so performance gaps conflate unseen perturbations with unseen forgeries.

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Cite this review

Pith. "Pith review of AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations." pith.science (2026). https://pith.science/paper/HYDZ4M2N

@misc{pith2026250720579,
  author       = {Pith},
  title        = {Pith review of: AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HYDZ4M2N}},
  note         = {Machine review of arXiv:2507.20579}
}
read the original abstract

The rapid surge of text-to-speech and face-voice reenactment models makes video fabrication easier and highly realistic. To encounter this problem, we require datasets that rich in type of generation methods and perturbation strategy which is usually common for online videos. To this end, we propose AV-Deepfake1M++, an extension of the AV-Deepfake1M having 2 million video clips with diversified manipulation strategy and audio-visual perturbation. This paper includes the description of data generation strategies along with benchmarking of AV-Deepfake1M++ using state-of-the-art methods. We believe that this dataset will play a pivotal role in facilitating research in Deepfake domain. Based on this dataset, we host the 2025 1M-Deepfakes Detection Challenge. The challenge details, dataset and evaluation scripts are available online under a research-only license at https://deepfakes1m.github.io/2025.

Figures

Figures reproduced from arXiv: 2507.20579 by the authors.

Figure 1
Figure 1. Comparison of LAV-DF, AV-Deepfake1M and AV [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Data generation pipeline of AV-Deepfake1M++. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The distribution of audio and visual perturbations in AV-Deepfake1M++. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The proportion statistics of source dataset in AV [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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Forward citations

Cited by 2 Pith papers

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

  1. HOLA: Enhancing Audio-visual Deepfake Detection via Hierarchical Contextual Aggregations and Efficient Pre-training

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A two-stage audio-visual deepfake detector, HOLA, uses 1.81M pre-training samples and hierarchical cross-modal fusion modules to achieve first place and near-perfect AUC on AV-Deepfake1M++ video-level detection.

  2. KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features

    eess.AS 2025-08 conditional novelty 4.0 of 10

    A challenge entry combining Wav2Vec-AASIST audio scores with lightweight handcrafted-feature video scores via calibration and maxout reports 92.78% AUC on AV-Deepfake1M++ testA.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.