Abstract: Mass surveillance systems for voice over IP (VoIP) conversations pose a great risk to privacy. These automated systems use learning models to analyze conversations, and calls that involve specific topics are routed to a human agent for further examination. In this study, we present an adversarial-learning-based framework for privacy protection for VoIP conversations. We present a novel method that finds a universal adversarial perturbation (UAP), which, when added to the audio stream, prevents an eavesdropper from automatically detecting the co...
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Topics: 
Computer security