Adversarial Machine Learning Against Voice Assistant Systems

Project Objective

This project aims to study the security of voice assistance systems under adversarial machine learning. The audio adversarial samples generated by adversarial learning algorithms can be played via a loudspeaker and recorded with the microphone of voice assistance systems so as to fool the machine learning models in the system. To make the adversarial samples robust under audio propagation, the room impulse response needs to be estimated and used in the adversarial sample generation process. Specifically, the room impulse response and adversarial attack scenarios can be conducted in digital domain or simulated for the over-the-air scenarios using Python or Matlab.

Tutorials

• Generating Adversarial Samples in Keras: ​https://medium.com/mindboard/generating-adversarial-samples-in-keras-tutorial-f881ac836246
• Tensorflow - Adversarial Example using FGSM: ​https://www.tensorflow.org/tutorials/generative/adversarial_fgsm
• Generating Adversarial Samples in Keras: ​https://medium.com/analytics-vidhya/implementing-adversarial-attacks-and-defenses-in-keras-tensorflow-2-0-cab6120c5715
• Python tutorial: ​https://www.w3schools.com/python/
• How to run Python code: ​https://www.knowledgehut.com/blog/programming/run-python-scripts
• Jupyter notebook tutorial: ​https://www.dataquest.io/blog/jupyter-notebook-tutorial/
• Video tutorial (Optional): Neural Networks and Deep Learning: ​https://www.coursera.org/learn/neural-networks-deep-learning

Reading Material

• ​Hidden voice commands
• ​CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
• ​Audio Adversarial Examples Targeted Attacks on Speech-to-Text
• ​Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
• ​Practical Adversarial Attacks Against Speaker Recognition Systems

Week 1 Activities

• Get ORBIT/COSMOS account and familiarize oneself with the testbed procedures

Week 2 Activities

• Get familiar with Python language.
  — Install Python environment
  — Use Jupyter Notebook to run Python code samples
  
• Learn the concept of deep learning and deep neural networks.
  — Slides: Neural Network Basics of Energy-Efficient Machine Learning System
  — Video tutorial (Optional): Neural Networks and Deep Learning by Andrew Ng (Recommended chapters: Week 2: Logistic Regression as a Neural Network, Week 3: Shallow Neural Network)