This repository is purposed to study couples of adversarial attack and defense methods not only by reading the papers but by exploring their actual code implementations.

• Explaining and Harnessing Adversarial Examples (ICLR 2015)
• Towards Evaluating the Robustness of Neural Networks (S&P 2017)
• Towards Deep Learning Models Resistant to Adversarial Attacks (ICLR 2018)
• Adversarial Examples Are Not Bugs, They Are Features (NIPS 2019)
• Adversarial Reprogramming of Neural Networks
• Hacking Neural Networks: A Short Introduction
• Adversarial Examples in the Physical World
• Fine-Pruning: Defending Against Backdooring Attacks on DNN
• Certified Robustness to Adversarial Examples with Differential Privacy (S&P 2019)
• Obfuscated Gradients Give a False Sense of Security (ICML 2018)
• Constructing Unrestricted Adversarial Examples with Generative Models (NIPS 2018)
• Deep Leackage from Gradients
• Adversarial Attacks: Attacks and Defenses for Deep Learning
• Adversarial Attacks and Defences Competition
• Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

Attack

• Fast Gradient Sign Method
• One-step Target Class Method
• Basic Iterative Method
• Iterative Target Class Method (Iterative Least-Likely Class Method)
• Projected Gradient Descent
• CW Attack
• Shadow Attack
• BPDA
• Constructing Unrestricted Adversarial Examples with Generative Models
• Advesarial Reprogramming of Neural Networks

Defence

• PGD Adversarial Training
• Pixel Differential Privacy

MORE

• cleverhans
• advertorch