adversarial-example(classify)

Gradient-based attacks

year/month	method	paper
2013-12	L-BFGS	Intriguing properties of neural networks
2014-12	FGSM	Explaining and Harnessing Adversarial Examples
2015-10	L-BFGS-B	Exploring the Space of Adversarial Images
2015-11	DeepFool	DeepFool: a simple and accurate method to fool deep neural networks
2015-11	JSMA	The Limitations of Deep Learning in Adversarial Settings
2016-07	PGD	Adversarial examples in the physical world
2016-08	C&W	Towards Evaluating the Robustness of Neural Networks
2017-06	BIM	Towards Deep Learning Models Resistant to Adversarial Attacks
2017-09	EADAttack	EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
2017-10	MomentumIterativeAttack	Boosting Adversarial Attacks with Momentum
2017-12	NewtonFoolAttack	Objective Metrics and Gradient Descent Algorithms for Adversarial Examples in Machine Learning
2018-04	ADefAttack	ADef: an Iterative Algorithm to Construct Adversarial Deformations
2018-11	DecoupledDirectionNormL2Attack	Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
2018-11	SparseFoolAttack	SparseFool: a few pixels make a big difference

year/month	method	paper
2016-12	LocalSearchAttack	Simple Black-Box Adversarial Perturbations for Deep Networks

year/month	method	paper
2017-12	SpatialAttack	A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
2017-12	BoundaryAttack	Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
2018-05	PointwiseAttack	Towards the first adversarially robust neural network model on MNIST
2019-06	PCAAttack	Sample Based Fast Adversarial Attack Method

year/month	method	paper	code
2017-03	DAG	Adversarial Examples for Semantic Segmentation and Object Detection	DAG
2018-07	RP2	Physical Adversarial Examples for Object Detectors	None
2018-09	RAP	Robust Adversarial Perturbation on Deep Proposal-based Models	RAP
2018-11	UEA	Transferable Adversarial Attacks for Image and Video Object Detection	UEA
2019-07	baidu	Towards Adversarially Robust Object Detection	None