CSI2Dig: Recovering Digit Content from Smartphone Loudspeakers Using Channel State Information

• URL: http://arxiv.org/abs/2504.14812v1
• Date: Mon, 21 Apr 2025 02:31:19 GMT
• Title: CSI2Dig: Recovering Digit Content from Smartphone Loudspeakers Using Channel State Information
• Authors: Yangyang Gu, Xianglong Li, Haolin Wu, Jing Chen, Kun He, Ruiying Du, Cong Wu,
• Abstract summary: We propose a scheme, CSI2Dig, for recovering digit content from Channel State Information (CSI) when digits are played through a smartphone loudspeaker.<n>We observe that the electromagnetic interference caused by the audio signals from the loudspeaker affects the WiFi signals emitted by the phone's WiFi antenna.<n>For feature extraction, we introduce the TS-Net, a model that captures relevant features from both the temporal and spatial dimensions of the CSI data.
• Score: 10.86045604075024
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Eavesdropping on sounds emitted by mobile device loudspeakers can capture sensitive digital information, such as SMS verification codes, credit card numbers, and withdrawal passwords, which poses significant security risks. Existing schemes either require expensive specialized equipment, rely on spyware, or are limited to close-range signal acquisition. In this paper, we propose a scheme, CSI2Dig, for recovering digit content from Channel State Information (CSI) when digits are played through a smartphone loudspeaker. We observe that the electromagnetic interference caused by the audio signals from the loudspeaker affects the WiFi signals emitted by the phone's WiFi antenna. Building upon contrastive learning and denoising autoencoders, we develop a two-branch autoencoder network designed to amplify the impact of this electromagnetic interference on CSI. For feature extraction, we introduce the TS-Net, a model that captures relevant features from both the temporal and spatial dimensions of the CSI data. We evaluate our scheme across various devices, distances, volumes, and other settings. Experimental results demonstrate that our scheme can achieve an accuracy of 72.97%.

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