论文标题
别再烦我了！使用对抗性扰动逃避现代窃听
Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations
论文作者
论文摘要
IP语音（VOIP）对话的大规模监视系统对隐私构成了极大的风险。这些自动化系统使用学习模型来分析对话，并将涉及特定主题的呼叫路由到人类代理以进行进一步检查。在这项研究中，我们提出了一个基于对抗性学习的框架，以保护VoIP对话的隐私保护。我们提出了一种新颖的方法，该方法可以找到通用的对抗扰动（UAP），该方法将其添加到音频流时，可以防止窃听者自动检测到对话的主题。如我们的实验所示，UAP对扬声器或音频长度不可知，并且可以根据需要实时更改其音量。我们的现实世界解决方案使用的是一个数字微控制器，该微控制器充当外部麦克风，并实时将UAP添加到音频中。我们检查了不同的扬声器，VoIP应用程序（Skype，Zoom，Slack和Google Meet）和音频长度。我们在现实世界中的结果表明，我们的方法是保护隐私保护的可行解决方案。
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.
