README.md

Setup

git clone https://github.com/DSE-MSU/DeepRobust.git
cd DeepRobust
python setup.py install

Attack Methods	Attack Type	Apply Domain	Links
LBFGS attack	White-Box	Image Classification	Intriguing Properties of Neural Networks
FGSM attack	White-Box	Image Classification	Explaining and Harnessing Adversarial Examples
PGD attack	White-Box	Image Classification	Towards Deep Learning Models Resistant to Adversarial Attacks
DeepFool attack	White-Box	Image Classification	DeepFool: a simple and accurate method to fool deep neural network
CW attack	White-Box	Image Classification	Towards Evaluating the Robustness of Neural Networks
One pixel attack	White-Box	Image Classification	One pixel attack for fooling deep neural networks
BPDA attack	White-Box	Image Classification	Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Universal attack	White-Box	Image Classification	Universal adversarial perturbations
Nattack	Black-Box	Image Classification	NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

Defense Methods	Defense Type	Apply Domain	Links
FGSM training	Adverserial Training	Image Classification	Towards Deep Learning Models Resistant to Adversarial Attacks
Fast(an improved version of FGSM training)	Adverserial Training	Image Classification	Fast is better than free: Revisiting adversarial training
PGD training	Adverserial Training	Image Classification	Intriguing Properties of Neural Networks
YOPO(an improved version of PGD training)	Adverserial Training	Image Classification	You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
TRADES	Adverserial Training	Image Classification	Theoretically Principled Trade-off between Robustness and Accuracy
Thermometer Encoding	Gradient Masking	Image Classification	Thermometer Encoding:One Hot Way To Resist Adversarial Examples
LID-based adversarial classifier	Detection	Image Classification	Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality