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Scores Know Bobs Voice: Speaker Impersonation Attack

arXiv – CS AI|Chanwoo Hwang, Sunpill Kim, Yong Kiam Tan, Tianchi Liu, Seunghun Paik, Dongsoo Kim, Mondal Soumik, Khin Mi Mi Aung, Jae Hong Seo|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed a new AI attack method that can fool speaker recognition systems with 10x fewer attempts than previous approaches. The technique uses feature-aligned inversion to optimize attacks in latent space, achieving up to 91.65% success rate with only 50 queries.

Key Takeaways

→New attack framework reduces query requirements by 10x compared to existing speaker impersonation methods.
→Feature-aligned inversion synchronizes latent space with speaker embeddings for more effective attacks.
→Subspace-projection-based attacks achieve 91.65% success rate using only 50 queries.
→The research exposes vulnerabilities in modern speaker recognition systems used for security.
→Findings provide a new tool for evaluating robustness of voice-based authentication systems.

#ai-security #speaker-recognition #adversarial-attacks #voice-authentication #cybersecurity #machine-learning #audio-ai #security-research

Read Original →via arXiv – CS AI

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