论文标题

GhostTalk:通过电源线对智能手机语音系统的交互式攻击

GhostTalk: Interactive Attack on Smartphone Voice System Through Power Line

论文作者

Wang, Yuanda, Guo, Hanqing, Yan, Qiben

论文摘要

声音命令注射是对语音助手的最威胁性攻击之一。现有的攻击旨在向空中注入攻击信号,但他们需要访问授权用户的声音以激活语音助手。此外,在嘈杂的环境中,攻击的有效性可能会大大恶化。在本文中,我们探索了一种新型的频道,即电源线侧通道,以启动听不清的语音命令注入。通过通过修改后的充电电缆在电源线上注入音频信号,对各种环境因素和易度检测模型的攻击变得更有弹性。同时,智能手机音频输出可以通过修改后的电缆窃听,从而实现高度相互交互的攻击。 为了利用电源线侧通道,我们提出了GhostTalk,这是一种新的隐藏语音攻击,能够同时注射和窃听。通过快速修改电源电缆,攻击者可以通过远程打电话或从语音助手那里捕获私人信息来发射交互式攻击。 GhostTalk通过偷偷触发开关组件来模拟耳机上的按钮来克服绕过扬声器验证系统的挑战。如果当智能手机通过不变的标准电缆充电时,我们发现可以通过监视电源线上的充电电流来从智能手机扬声器中恢复音频信号。为了证明可行性,我们设计了GhostTalk-SC,这是一种针对公共USB端口中智能手机的自适应窃听系统。要正确识别音频中的私人信息,GhostTalk-SC仔细提取音频光谱并集成了神经网络模型,以对语音中的口头数字进行分类。

Inaudible voice command injection is one of the most threatening attacks towards voice assistants. Existing attacks aim at injecting the attack signals over the air, but they require the access to the authorized user's voice for activating the voice assistants. Moreover, the effectiveness of the attacks can be greatly deteriorated in a noisy environment. In this paper, we explore a new type of channel, the power line side-channel, to launch the inaudible voice command injection. By injecting the audio signals over the power line through a modified charging cable, the attack becomes more resilient against various environmental factors and liveness detection models. Meanwhile, the smartphone audio output can be eavesdropped through the modified cable, enabling a highly-interactive attack. To exploit the power line side-channel, we present GhostTalk, a new hidden voice attack that is capable of injecting and eavesdropping simultaneously. Via a quick modification of the power bank cables, the attackers could launch interactive attacks by remotely making a phone call or capturing private information from the voice assistants. GhostTalk overcomes the challenge of bypassing the speaker verification system by stealthily triggering a switch component to simulate the press button on the headphone. In case when the smartphones are charged by an unaltered standard cable, we discover that it is possible to recover the audio signal from smartphone loudspeakers by monitoring the charging current on the power line. To demonstrate the feasibility, we design GhostTalk-SC, an adaptive eavesdropper system targeting smartphones charged in the public USB ports. To correctly recognize the private information in the audio, GhostTalk-SC carefully extracts audio spectra and integrates a neural network model to classify spoken digits in the speech.

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