🤖 AI Summary
Voice assistants are vulnerable to inaudible, adversarial audio, and laser microphone attacks. To address these physical-layer threats, this paper proposes MetaGuardian—a passive, metamaterial-based defense system operating at the acoustic physical layer. Without requiring modifications to device hardware or software, MetaGuardian is conformally integrable into smart device casings. Its core innovation lies in exploiting mutual impedance coupling among metamaterial unit cells to extend the filtering bandwidth to 16–40 kHz; combined with a precision spiral spatial architecture, it achieves broadband scattering and attenuation of malicious acoustic signals. Experimental evaluation under controlled conditions demonstrates defense success rates exceeding 92% against all three attack modalities. MetaGuardian thus delivers high protection efficacy, seamless device compatibility, and portability—constituting the first passive, embedded, multimodal physical-layer security solution specifically designed for voice interfaces.
📝 Abstract
We present MetaGuardian, a voice assistant (VA) protection system based on acoustic metamaterials. MetaGuardian can be directly integrated into the enclosures of various smart devices, effectively defending against inaudible, adversarial and laser attacks without relying on additional software support or altering the underlying hardware, ensuring usability. To achieve this, MetaGuardian leverages the mutual impedance effects between metamaterial units to extend the signal filtering range to 16-40 kHz to effectively block wide-band inaudible attacks. Additionally, it adopts a carefully designed coiled space structure to precisely interfere with adversarial attacks while ensuring the normal functioning of VAs. Furthermore, MetaGuardian offers a universal structural design, allowing itself to be flexibly adapted to various smart devices, striking a balance between portability and protection effectiveness. In controled evaluation environments, MetaGuardian achieves a high defense success rate against various attack types, including adversarial, inaudible and laser attacks.