= Adversarial Machine Learning Against Voice Assistant Systems =

== 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, we 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. If time allows, we will further enhance the robustness of the attack by simulating room impulse response and conduct over-the-air attack.\\
-- [https://www.orbit-lab.org/attachment/wiki/Other/Summer/2020/AdvML/Weekly%20plan%20for%20adversarial%20machine%20learning%20against%20voice%20assistant%20systems.docx Weekly plan]
== Tutorials ==
*Week 1
- 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
*Week 2
- 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
*Week 3
- Introduction of Keras: https://en.wikipedia.org/wiki/Keras
- Basic Classification: Classify Images of Clothing: https://www.tensorflow.org/tutorials/keras/classification
- Simple Neural Networks in Python: https://towardsdatascience.com/inroduction-to-neural-networks-in-python-7e0b422e6c24
- TensorFlow Neural Network Tutorial (optional): https://stackabuse.com/tensorflow-neural-network-tutorial/

== Reading Material ==
- [https://www.orbit-lab.org/attachment/wiki/Other/Summer/2020/AdvML/Hidden%20voice%20commands.pdf Hidden voice commands]
- [https://www.orbit-lab.org/attachment/wiki/Other/Summer/2020/AdvML/Commandersong%20A%20systematic%20approach%20for%20practical%20adversarial%20voice%20recognition.pdf CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition]
- [https://www.orbit-lab.org/attachment/wiki/Other/Summer/2020/AdvML/Audio%20Adversarial%20Examples%20Targeted%20Attacks%20on%20Speech-to-Text.pdf Audio Adversarial Examples Targeted Attacks on Speech-to-Text]
- [https://www.orbit-lab.org/attachment/wiki/Other/Summer/2020/AdvML/Imperceptible%2C%20Robust%2C%20and%20Targeted%20Adversarial%20Examples%20for%20Automatic%20Speech%20Recognition.pdf Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition]
- [https://www.orbit-lab.org/attachment/wiki/Other/Summer/2020/AdvML/Practical%20Adversarial%20Attacks%20Against%20Speaker%20Recognition%20Systems.pdf Practical Adversarial Attacks Against Speaker Recognition Systems]
- [https://www.orbit-lab.org/attachment/wiki/Other/Summer/2020/AdvML/X-VECTORS-%20ROBUST%20DNN%20EMBEDDINGS%20FOR%20SPEAKER%20RECOGNITION.pdf X-Vectors: Robust DNN Embeddings For Speaker Recognition]
== 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)

== Week 3 Activities ==
- Setup TensorFlow and Keras environment using Anaconda \\
  -- Follow the tutorial “Basic classification: Classify Images of Clothing” to get familiar with TensorFlow and Keras \\
  -- Read the tutorial “Simple Neural Networks in Python” (code implementation not required) \\
  -- Read the “TensorFlow Neural Network Tutorial” and run the code implementation (optional) 

- Read the paper “X-Vectors: Robust DNN Embeddings for Speaker Recognition” (IEEE ICASSP 2018). \\
  -- Try to understand the workflow of x-vector and learn background knowledge, such as the application of x-vector, concept of the phoneme, data augmentation, etc. (try to learn TDNN and MFCC if time allows)

== Project Website ==
- [https://chunnubansal.wixsite.com/winlab-amlavas]