An audio system containing loudspeakers and microphones is the fundamental hardware for voice-enabled devices, enabling voice interaction with mobile applications and smart homes. This paper presents MagBackdoor, the first magnetic field attack that injects malicious commands via a loudspeaker-based backdoor of the audio system, compromising the linked voice interaction system. MagBackdoor focuses on the magnetic threat on loudspeakers and manipulates their sound production stealthily. Consequently, the microphone will inevitably pick up malicious sound generated by the attacked speaker, due to the closely packed arrangement of internal audio systems. To prove the feasibility of MagBackdoor, we conduct comprehensive simulations and experiments. This study further models the mechanism by which an external magnetic field excites the sound production of loudspeakers, giving theoretical guidance to MagBackdoor. Aiming at stealthy magnetic attacks in real-world scenarios, we self-design a prototype that can emit magnetic fields modulated by voice commands. We implement MagBackdoor and evaluate it across a wide range of smart devices involving 16 smartphones, four laptops, two tablets, and three smart speakers, achieving an average 95% injection success rate with high-quality injected acoustic signals.

Fingerprint recognition technology is the most widely used technology in the field of biometrics and is also the preferred solution for mobile devices and the financial industry. However, traditional fingerprint recognition technology requires expensive sensors on the one hand and risks leaking fingerprint images on the other hand. In this paper, we propose Fingersound, a low-cost and deployable authentication system that utilizes the sound generated by finger sliding as an identity feature. We use a microphone array to record continuous sliding sound and extract multiple features in the frequency and time domains. Then we use various algorithms including a deep neural network to perform user authentication. We also design a mobile phone application that can interact with the embedded system to manage users and record authentication history. In our experiments, 100 participants used this system and achieved an equal error rate of 5.2%. Additionally, we investigate the system’s robustness within different sliding materials, texturesand under noise disturbances. We further demonstrate the resistance of Fingersound to replay attack.