SaTC: CORE: Small: Securing IoT and Edge Devices under Audio Adversarial Attacks

Dates: 10/01/2021 – 09/30/2024
Award Amount: $330,000
Award #2114220

PI: Yingying Chen
Co-PI: Bo Yuan 


Powered by the advancement of artificial intelligence (AI) techniques, the next-generation voice-controllable IoT and edge systems have substantially facilitated people’s daily lives. Such systems include voice assistant systems and voice authenticated mobile banking, among many others. However, the underlying machine learning approaches used in these systems, are inherently vulnerable to audio adversarial attacks, in which an adversary can mislead the machine learning models via injecting imperceptible perturbation to the original audio input. Given the widespread adoption of voice-controllable IoT and edge systems in many privacy-critical and safety-critical applications, e.g., personal banking and autonomous driving, the in-depth understanding and investigation of severity and consequences of audio-based adversarial attack as well as the corresponding defense solutions, are highly demanded. This project will perform a comprehensive study and analysis of the vulnerability and robustness of voice-controllable IoT and edge systems against audio-domain adversarial attacks in both temporal and spatial perspectives. The research outcome of this project will form solid foundations for building trustworthy voice-controllable IoT and edge systems. The developed defense techniques will improve the security of many intelligent audio systems, such as automatic speech recognition (ASR), keyword spotting, and speaker recognition. This project will involve underrepresented students, undergraduate and graduate students, and K-12 students through a variety of engaging programs.

The objective of this project is to demonstrate the feasibility of audio adversarial attacks in the physical world, determine the attack severity and consequences, and further develop defending strategies in practical environments to build attack-resilient voice-controllable Internet-of-Things (IoT) devices and edge systems. To study the possibility and severity of audio adversarial attacks in a practical time-constraint setting, the project will develop low-cost audio-agnostic synchronization-free attack launching schemes, including audio-specific fast adversarial perturbation generator and universal adversarial perturbation generator. To investigate how the adversarial perturbations survive various propagation factors in realistic environments, the project will analyze the audio distortions caused by the over-the-air propagation using an advanced room impulse response simulator and physical environment measurements. The project will also develop several defense techniques, including defensive denoiser, model enhancement, and microphone-array-based liveness detection. The presented technique will help to secure the voice-controllable IoT and edge devices under audio adversarial attacks. The project will also contribute to a new computing paradigm in audio-based adversarial machine learning in both theoretic foundations as well as safety-critical audio-oriented emerging applications.