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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 1571–1580 of 1808 papers

Title	Date	Tasks	Status	Hype
Universal Adversarial Attack Using Very Few Test Examples	Sep 25, 2019	Adversarial Attack	—Unverified	0
Learning Key Steps to Attack Deep Reinforcement Learning Agents	Sep 25, 2019	Adversarial AttackAtari Games	—Unverified	0
Robust saliency maps with distribution-preserving decoys	Sep 25, 2019	Adversarial Attack	—Unverified	0
SELF-KNOWLEDGE DISTILLATION ADVERSARIAL ATTACK	Sep 25, 2019	Adversarial AttackKnowledge Distillation	—Unverified	0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images	Sep 25, 2019	Adversarial AttackRepresentation Learning	—Unverified	0
Simple and Effective Stochastic Neural Networks	Sep 25, 2019	Adversarial AttackAdversarial Defense	—Unverified	0
THE EFFECT OF ADVERSARIAL TRAINING: A THEORETICAL CHARACTERIZATION	Sep 25, 2019	Adversarial Attack	—Unverified	0
Towards Certified Defense for Unrestricted Adversarial Attacks	Sep 25, 2019	Adversarial Attack	—Unverified	0
Adversarial training with perturbation generator networks	Sep 25, 2019	Adversarial Attack	—Unverified	0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex	Sep 25, 2019	Adversarial AttackBayesian Inference	—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