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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 1461–1470 of 1808 papers

Title	Date	Tasks	Status	Hype
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack	Jun 12, 2020	Adversarial Attack	—Unverified	0
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors	Jun 11, 2020	Adversarial AttackMedical Image Analysis	CodeCode Available	0
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples	Jun 11, 2020	Adversarial Attack	—Unverified	0
Global Robustness Verification Networks	Jun 8, 2020	Adversarial Attack	—Unverified	0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images	Jun 1, 2020	Adversarial AttackAdversarial Text	—Unverified	0
ILFO: Adversarial Attack on Adaptive Neural Networks	Jun 1, 2020	Adversarial Attack	—Unverified	0
Modeling Biological Immunity to Adversarial Examples	Jun 1, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention	Jun 1, 2020	Adversarial AttackObject	—Unverified	0
Polishing Decision-Based Adversarial Noise With a Customized Sampling	Jun 1, 2020	Adversarial Attackimage-classification	—Unverified	0
Robust Superpixel-Guided Attentional Adversarial Attack	Jun 1, 2020	Adversarial AttackSteganalysis	—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