Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations

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-le...

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Bibliographic Details
Published in:arXiv.org 2021-09
Main Authors: Mathov, Yael, Tal Ben Senior, Shabtai, Asaf, Elovici, Yuval
Format: Article
Language:English
Subjects:
Algorithms
Audio equipment
Learning
Microcontrollers
Perturbation
Privacy
Real time
Speech recognition
Surveillance systems
Voice communication
Wiretapping
Online Access:Get full text
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Summary: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 conversation's topic. As shown in our experiments, the UAP is agnostic to the speaker or audio length, and its volume can be changed in real time, as needed. Our real-world solution uses a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real time. We examine different speakers, VoIP applications (Skype, Zoom, Slack, and Google Meet), and audio lengths. Our results in the real world suggest that our approach is a feasible solution for privacy protection.
ISSN:2331-8422