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Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1621–1630 of 1808 papers

Title	Date	Tasks	Status	Hype
Exploring the Robustness of NMT Systems to Nonsensical Inputs	Aug 3, 2019	Adversarial AttackDecoder	—Unverified	0
Adversarial Attack on Sentiment Classification	Aug 1, 2019	Adversarial AttackClassification	—Unverified	0
Black-box Adversarial ML Attack on Modulation Classification	Aug 1, 2019	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity	Jul 30, 2019	Adversarial Attack	—Unverified	0
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method	Jul 26, 2019	Adversarial AttackBayesian Optimization	CodeCode Available	0
Affine Disentangled GAN for Interpretable and Robust AV Perception	Jul 6, 2019	Adversarial AttackAutonomous Vehicles	—Unverified	0
Adversarial Attacks in Sound Event Classification	Jul 4, 2019	Adversarial AttackClassification	—Unverified	0
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack	Jul 3, 2019	Adversarial Attack	CodeCode Available	0
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"	Jul 1, 2019	Adversarial AttackAdversarial Defense	—Unverified	0
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency	Jul 1, 2019	Adversarial AttackClassification	CodeCode Available	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified