arXiv:2603.07551v2 Announce Type: replace-cross Abstract: Zero-shot Text-to-Speech (TTS) voice cloning poses severe privacy risks, demanding the removal of specific speaker identities from trained TTS models. Conventional machine unlearning is insufficient in this context, as zero-shot TTS can dynamically reconstruct voices from just reference prompts. We formalize this task as Speech Generation Speaker Poisoning (SGSP), in which we modify trained models to prevent the generation of specific identities while preserving utility for other speakers. We evaluate inference-time filtering and parame
The proliferation of advanced AI capabilities like zero-shot Text-to-Speech (TTS) necessitates parallel innovation in managing and mitigating their associated risks, particularly concerning privacy and identity. This urgency is driven by rapid advancements in generative AI and increasing societal awareness of its potential misuse.
This development addresses critical privacy and security vulnerabilities in advanced AI, specifically preventing the misuse of voice cloning. For strategic readers, it highlights the essential co-development of AI capabilities and robust control mechanisms, which will be crucial for the ethical deployment and public acceptance of future AI systems.
The ability to specifically 'unlearn' or poison a model against generating particular speaker identities, while maintaining its overall utility, introduces a new paradigm for AI safety and privacy. This changes how AI models can be governed and how personal data within them could be protected.
- · AI ethics and safety researchers
- · Individuals concerned about voice identity theft
- · Developers of secure AI applications
- · Regulatory bodies developing AI guidelines
- · Malicious actors attempting voice impersonation
- · Developers of unsecure AI voice systems
- · Black hat AI researchers
The immediate effect is a more secure method for removing specific identities from trained zero-shot TTS models.
This framework could lead to broader applications of 'unlearning' and 'poisoning' techniques for other sensitive data in AI models, enhancing overall data privacy.
The increased trust in AI systems due to better privacy controls could accelerate widespread adoption of voice-driven AI interfaces in sensitive sectors like banking or healthcare.
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Read at arXiv cs.AI