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New AV-FABLE benchmark targets AI manipulation forensics in long videos

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced AV-FABLE, a new benchmark designed to detect sparse manipulations in long-form audio-visual videos. Unlike existing benchmarks that focus on short clips, AV-FABLE utilizes 16,650 pristine-manipulated video pairs to evaluate forensic analysis capabilities. The study found that while current audio-visual large language models (AV-LLMs) struggle with zero-shot detection in long videos, their frozen perception features contain significant forensic signals that can be recovered using separate temporal decoders. AI

IMPACT This benchmark could improve the detection of sophisticated AI-generated manipulations in longer video content.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for AI manipulation forensics.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New AV-FABLE benchmark targets AI manipulation forensics in long videos

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The cluster describes a new academic paper introducing a benchmark for AI manipulation forensics.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    AV-FABLE: Audio-Visual Manipulation Forensics in Long-Form Videos

    AV-FABLE introduces a long-form audio-visual benchmark for sparse manipulations, showing that frozen perception features of audio-visual large language models contain forensic evidence best recovered via separate temporal decoders rather than language outputs.

  2. arXiv cs.CV TIER_1 English(EN) · Yuesheng Huang, Hoi Fan Au, Jiaqi Hou, Ruicheng Zhang, Jiarui He, Haocheng Yang, Bin Hu, Kewei Zhang, Weijian Deng, Jun Zhou, Xiu Li ·

    AV-FABLE: Audio-Visual Manipulation Forensics in Long-Form Videos

    arXiv:2610.33413v1 Announce Type: new Abstract: Localized AI manipulations in long-form videos often affect brief events within otherwise authentic content, making video-level classification insufficient for forensic analysis. Existing audio-visual deepfake benchmarks largely foc…

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