arXiv:2606.10908v1 Announce Type: cross Abstract: We introduce a spoofing countermeasure architecture conditioned on speaker-reference recordings, but observe that it converges to a solution that effectively ignores the reference during inference. Surprisingly, training with a reference channel induces invariance that improves deepfake detection, even when the reference is absent or mismatched during inference. Based on this observation, we propose a Reference-Augmented Training (RAT) strategy. RAT yields improved detection performance compared to single-utterance baselines, even when the refe
The proliferation of advanced AI-driven audio synthesis makes robust anti-spoofing countermeasures increasingly critical for security and authentication systems.
Improved deepfake detection methods are vital for maintaining trust in digital voice communications and securing systems reliant on voice biometrics against sophisticated attacks.
New training methodologies for ASV anti-spoofing can significantly enhance the accuracy and resilience of voice deepfake detection, even with limited reference data.
- · Cybersecurity sector
- · Voice biometric providers
- · Financial institutions
- · Government security agencies
- · Deepfake creators
- · Fraudsters using voice spoofing
Security for voice-controlled systems and biometric authentication improves, reducing fraud risk.
Increased confidence in voice-based interactions could accelerate adoption of voice interfaces in sensitive applications.
This could lead to an arms race between deepfake generation and detection, where both technologies continually advance.
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Read at arXiv cs.LG