Biblio

Voice-based input is usually used as the primary input method for augmented reality (AR) headsets due to immersive AR experience and good recognition performance. However, recent researches have shown that an attacker can inject inaudible voice commands to the devices that lack voice verification. Even if we secure voice input with voice verification techniques, an attacker can easily steal the victim's voice using low-cast handy recorders and replay it to voice-based applications. To defend against voice-spoofing attacks, AR headsets should be able to determine whether the voice is from the person who is using the AR headsets. Existing voice-spoofing defense systems are designed for smartphone platforms. Due to the special locations of microphones and loudspeakers on AR headsets, existing solutions are hard to be implemented on AR headsets. To address this challenge, in this paper, we propose a voice-spoofing defense system for AR headsets by leveraging both the internal body propagation and the air propagation of human voices. Experimental results show that our system can successfully accept normal users with average accuracy of 97% and defend against two types of attacks with average accuracy of at least 98%.

Smartwatches are expected to become the world's best-selling electronic product after smartphones. Various smart-watches have been released to the private consumer market, but the data on smartwatches is not well protected. In this paper, we show for the first time that photoplethysmography (PPG)signals influenced by hand gestures can be used to authenticate users on smartwatches. The insight is that muscle and tendon movements caused by hand gestures compress the arterial geometry with different degrees, which has a significant impact on the blood flow. Based on this insight, novel approaches are proposed to detect the starting point and ending point of the hand gesture from raw PPG signals and determine if these PPG signals are from a normal user or an attacker. Different from existing solutions, our approach leverages the PPG sensors that are available on most smartwatches and does not need to collect training data from attackers. Also, our system can be used in more general scenarios wherever users can perform hand gestures and is robust against shoulder surfing attacks. We conduct various experiments to evaluate the performance of our system and show that our system achieves an average authentication accuracy of 96.31 % and an average true rejection rate of at least 91.64% against two types of attacks.