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New dataset and method tackle multi-region speech inpainting forensics

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method and dataset called MIST to address the challenge of detecting and localizing multiple manipulated segments within audio deepfakes. Existing methods struggle with partial speech manipulation where only a small percentage of an utterance is altered. The proposed ISA framework analyzes audio in a coarse-to-fine manner, identifying all tampered regions without needing to know their number beforehand. This approach is crucial as current deepfake detectors fail to flag audio with minimal manipulated content. AI

IMPACT Advances audio deepfake detection, crucial for combating misinformation and ensuring authenticity in spoken content.

RANK_REASON The cluster describes a new academic paper introducing a dataset, method, and metric for audio deepfake forensics.

Read on arXiv cs.CV →

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

New dataset and method tackle multi-region speech inpainting forensics

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The cluster describes a new academic paper introducing a dataset, method, and metric for audio deepfake forensics.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Tung Vu, Yen Nguyen, Hai Nguyen, Cuong Pham, Cong Tran ·

    Toward Fine-Grained Speech Inpainting Forensics:A Dataset, Method, and Metric for Multi-Region Tampering Localization

    arXiv:2605.02223v1 Announce Type: cross Abstract: Recent advances in voice cloning and text-to-speech synthesis have made partial speech manipulation - where an adversary replaces a few words within an utterance to alter its meaning while preserving the speaker's identity - an in…

  2. arXiv cs.CV TIER_1 English(EN) · Cong Tran ·

    Toward Fine-Grained Speech Inpainting Forensics:A Dataset, Method, and Metric for Multi-Region Tampering Localization

    Recent advances in voice cloning and text-to-speech synthesis have made partial speech manipulation - where an adversary replaces a few words within an utterance to alter its meaning while preserving the speaker's identity - an increasingly realistic threat. Existing audio deepfa…

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