GhostTalk: Interactive Attack on Smartphone Voice System Through Power Line
Yuanda Wang, Hanqing Guo, Qiben Yan
摘要
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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引用它的顶会 Paper17
- Exploiting Contactless Side Channels in Wireless Charging Power Banks for User Privacy Inference via Few-shot LearningTao Ni, Jianfeng Li, Xiaokuan Zhang, Chaoshun Zuo 等MobiCom 2023 · 被引用 27 次
- SPECPATCH: Human-In-The-Loop Adversarial Audio Spectrogram Patch Attack on Speech RecognitionHanqing Guo, Yuanda Wang, Nikolay Ivanov, Li Xiao 等CCS 2022 · 被引用 22 次
- Federated IoT Interaction Vulnerability AnalysisGuangjing Wang, Hanqing Guo, Anran Li, Xiaorui Liu 等ICDE 2023 · 被引用 20 次
- "Get in Researchers; We're Measuring Reproducibility": A Reproducibility Study of Machine Learning Papers in Tier 1 Security ConferencesDaniel Olszewski, Allison Lu, Carson Stillman, Kevin Warren 等CCS 2023 · 被引用 19 次
- XPorter: A Study of the Multi-Port Charger Security on Privacy Leakage and Voice InjectionTao Ni, Yongliang Chen, Weitao Xu, Lei Xue 等MobiCom 2023 · 被引用 15 次
它引用的顶会 Paper7
- DolphinAttack: Inaudible Voice CommandsGuoming Zhang, Chen Yan, Xiaoyu Ji, Tianchen Zhang 等CCS 2017 · 被引用 753 次
- Speechless: Analyzing the Threat to Speech Privacy from Smartphone Motion SensorsS. Abhishek Anand, Nitesh SaxenaS&P 2018 · 被引用 110 次
- Charger-Surfing: Exploiting a Power Line Side-Channel for Smartphone Information LeakagePatrick Cronin, Xing Gao, Chengmo Yang, Haining WangUSENIX Security 2021 · 被引用 62 次
- Wireless Charging Power Side-Channel AttacksAlexander S. La Cour, Khurram K. Afridi, G. Edward SuhCCS 2021 · 被引用 40 次
- Light Commands: Laser-Based Audio Injection Attacks on Voice-Controllable SystemsTakeshi Sugawara, Benjamin Cyr, Sara Rampazzi, Daniel Genkin 等USENIX Security 2020
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