Safespeech: Robust And Universal Voice Protection Against Malicious Speech Synthesis

Abstract

Speech synthesis technology has brought great convenience, while the widespread usage of realistic deepfake audio has triggered hazards. Malicious adversaries may unauthorizedly collect victims' speeches and clone a similar voice for illegal exploitation (\textit\{e.g.\}, telecom fraud). However, the existing defense methods cannot effectively prevent deepfake exploitation and are vulnerable to robust training techniques. Therefore, a more effective and robust data protection method is urgently needed. In response, we propose a defensive framework, \textit\{\textbf\{SafeSpeech\}\}, which protects the users' audio before uploading by embedding imperceptible perturbations on original speeches to prevent high-quality synthetic speech. In SafeSpeech, we devise a robust and universal proactive protection technique, \textbf\{S\}peech \textbf\{PE\}rturbative \textbf\{C\}oncealment (\textbf\{SPEC\}), that leverages a surrogate model to generate universally applicable perturbation for generativ

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