Adversarial Machine Learning Against Voice Assistant Systems

WINLAB Summer Internship 2022

Group Members: Matt Kokolus, Catherine Mathews

Project Objective

This project aims to study the security of voice assistant systems under adversarial machine learning. Adversarial learning algorithms can generate adversarial audio samples to serve as the input of voice assistant systems, so as to fool the machine learning models in the system. In this project, students will focus on the white-box attack in the digital domain by generating adversarial samples using adversarial machine learning algorithms to attack a speaker recognition system based on X-Vector. The students will learn Python with Tensorflow Library.

Week 1

• Defined project goals
• Created plan of action for future weeks

Week 2

• Read and studied paper: 'Practical Adversarial Attacks Against Speaker Recognition Systems'
• Changed project objectives to focus on automating drone flight and testing with the ultimate goal of automating drone flight to potentially fly with voice commands
• Became familiar with Yuneec Tornado H920 Pro Drone

Week 3

• Began Udemy course: 'Complete Guide to Tensorflow for Deep Learning with Python'
• Learned how to set up the drone (charging batteries, calibrating GPS, etc)
• Practiced flying the drone

Week 4

• Continued working on Udemy course to learn Tensorflow
• Changed primary drone to Holy Stone hs700d

Week 5

• Completed Udemy course
• Read three papers to understand audio preprocessing: 'Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems', 'AdvPulse: Universal, Synchronization-free, and Targeted Audio Adversarial Attacks via Sub-second Perturbations', and 'Enabling Fast and Universal Audio Adversarial Attack Using Generative Model'