We present METAATTACK, the first approach to leverage acoustic metamaterials for inaudible attacks for voice control systems. Compared to the state-of-the-art inaudible attacks requiring complex and large speaker setups, METAATTACK achieves a longer attacking range and higher accuracy using a compact, portable device small enough to be put into a carry bag. These improvements in portability and stealth have led to the practical applicability of inaudible attacks and their adaptation to a wider range of scenarios. We demonstrate how the recent advancement in metamaterials can be utilized to design a voice attack system with carefully selected implementation parameters and commercial off-the-shelf components. We showcase that METAATTACK can be used to launch inaudible attacks for representative voice-controlled personal assistants, including Siri, Alexa, Google Assistant, XiaoAI, and Xiaoyi. The average success rate of all assistants is 76%, with a range of 8.85 m.

Acoustic metasurface was recently proposed to enhance the performance of acoustic communication and sensing. While promising, there are two issues hindering the adoption of acoustic metasurface for real-life usage. The first issue is that configurable metasurface is still expensive and unscalable. The second issue is that it is difficult for an acoustic metasurface to work in a large frequency range. In this paper, we present a wideband and configurable acoustic metasurface for the first time. We show that with a large number of metasurface elements, a cheap and simple two-state element design can achieve performance very close to that achieved by expensive continuous-state elements. We also fine-tune the geometric parameter of the element structure to support similar phase changes across a large frequency range, laying the foundation to enable wideband acoustic metasurface. Extensive experiments show that our system can achieve an average signal strength improvement of 7.5 dB and 10.5 dB in LoS and NLoS scenarios respectively with the help of a metasurface with a size of 17.6 × 17.6 cm. Two representative sensing applications (i.e., respiration sensing and gesture recognition) and one communication case study are employed to show the effectiveness of the metasurface.

Wireless localization technology is a vital component in many long-term monitoring applications, such as activity monitoring and real-time tracking. Most existing localization methods however require the target to carry communicationcapable devices to send or receive messages, which may not hold for wildlife monitoring or intrusion detection. Prior proposals are based on device-free localization techniques, such as Channel State Information (CSI). However, they cost huge human effort in fingerprint collection when locate the target in different scenarios with different area size. This paper proposes a robust and accurate at low-cost devicefree localization system named LiReT. To reduce the time cost and human effort in fingerprint collection when the monitoring environment changed, we represent a LiReT algorithm based on a multivariable linear regression model to transfer the CSI measurements (fingerprint) at distance L to L'. Thus, LiReT can locate the target accurately at low-cost. Result from experiments demonstrate that our system can improve the localization accuracy by up to 51.68%, which is competitive with existing solutions.