This is a black-box attack library.

Attack Methods

Attack Methods	Attack Type	Similarity	Links
Zoo	score based	L_2	ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
Nattack	score based	L_inf	NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
SimBA	score based	L_2	Simple Black-box Adversarial Attacks
Bandit	score based	L_2	Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors)
Boundary Attack	decision based	-	Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
HSJA	decision based	-	HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
QEBA	decision based	-	QEBA: Query-Efficient Boundary-Based Blackbox Attack
Bayes Attack	decison based	-	Simple Black-box Adversarial Attacks

Datasets

Currently these attacks are only test on MNIST and CIFAR10.