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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 1421–1430 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Eigen Attack on Black-Box Models	Aug 27, 2020	Adversarial Attack	—Unverified	0
SIGL: Securing Software Installations Through Deep Graph Learning	Aug 26, 2020	Adversarial AttackDecoder	—Unverified	0
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition	Aug 26, 2020	Adversarial AttackData Augmentation	—Unverified	0
An Adversarial Attack Defending System for Securing In-Vehicle Networks	Aug 25, 2020	Adversarial Attack	—Unverified	0
PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards	Aug 24, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses	Aug 21, 2020	Adversarial AttackThompson Sampling	—Unverified	0
A New Perspective on Stabilizing GANs training: Direct Adversarial Training	Aug 19, 2020	Adversarial AttackImage Generation	CodeCode Available	0
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization	Aug 18, 2020	Adversarial Attack	—Unverified	0
Improving adversarial robustness of deep neural networks by using semantic information	Aug 18, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Model Robustness with Text Classification: Semantic-preserving adversarial attacks	Aug 12, 2020	Adversarial AttackClassification	—Unverified	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