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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 1351–1360 of 1808 papers

Title	Date	Tasks	Status	Hype
A black-box adversarial attack for poisoning clustering	Sep 9, 2020	Adversarial AttackClustering	CodeCode Available	0
Adversarial Attack on Large Scale Graph	Sep 8, 2020	Adversarial Attack	CodeCode Available	1
Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method	Sep 7, 2020	Adversarial AttackImage Reconstruction	—Unverified	0
Adversarially Robust Neural Architectures	Sep 2, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarial Eigen Attack on Black-Box Models	Aug 27, 2020	Adversarial Attack	—Unverified	0
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition	Aug 26, 2020	Adversarial AttackData Augmentation	—Unverified	0
SIGL: Securing Software Installations Through Deep Graph Learning	Aug 26, 2020	Adversarial AttackDecoder	—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

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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