What Counts as Real? Speech Restoration and Voice Quality Conversion Pose New Challenges to Deepfake Detection
arXiv:2603.14033v2 Announce Type: replace-cross Abstract: Audio anti-spoofing systems are typically trained to assign one authenticity label to an entire speech utterance. This formulation becomes under-specified for transformations where the underlying speaker identity and linguistic content remain unchanged. We study this problem using benign, authenticity-preserving speech transformations, including voice quality conversion and speech restoration, applied to both bona fide and spoofed speech. Instead of treating all processed audio as spoofed, we factorise labels into source authenticity an
The rapid advancement of deepfake technologies and sophisticated audio manipulation necessitates more adaptive detection methods, driven by accessible AI tools.
This research highlights the evolving challenge of distinguishing authentic human speech from AI-generated or manipulated audio, impacting security, trust, and intelligence operations.
Deepfake detection systems must evolve beyond simple authenticity labels to factorize transformations, creating a more nuanced understanding of audio provenance.
- · AI anti-spoofing developers
- · Cybersecurity sector
- · Forensic audio analysts
- · Unsophisticated deepfake detection systems
- · Individuals relying on basic audio authenticity checks
Improved deepfake detection methods will emerge that can better differentiate between benign audio transformations and malicious spoofs.
The development of 'authenticity metrics' rather than binary labels for audio, leading to more granular trust assessments.
This could contribute to the development of real-time audio provenance tracking and blockchain-secured audio authenticity records.
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Read at arXiv cs.AI