arXiv:2606.16532v1 Announce Type: cross Abstract: Audio deepfake detectors often fail to generalize across speakers, as they learn speaker-identity features rather than synthesis artifacts, known as implicit identity leakage. Existing methods address this but incur architectural complexity or training instability. This paper proposes a dual-granularity orthogonal disentanglement framework enforcing feature independence at two levels: sample-level cosine orthogonality captures directional decorrelation, while batch-level cross-covariance regularization eliminates linear correlations across embe
The proliferation of sophisticated audio deepfake generation techniques necessitates advanced detection mechanisms to maintain trust and security in digital audio environments.
Improved deepfake detection is crucial for mitigating the risks of misinformation, fraud, and identity manipulation, especially as AI-generated content becomes more prevalent and realistic.
The ability to more effectively distinguish between genuine and synthetic audio, particularly across different speakers, reduces a significant vulnerability in current detection systems.
- · Cybersecurity firms
- · Digital audio platforms
- · Law enforcement
- · Public trust in information
- · Deepfake creators
- · Disinformation actors
More robust and generalizable audio deepfake detection systems will become available.
This advancement could lead to a 'deepfake arms race' where generation and detection technologies constantly evolve against each other.
Increased reliability in audio authenticity may foster greater public confidence in digital media, potentially impacting online interactions and legal evidence.
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Read at arXiv cs.AI