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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 1591–1600 of 1808 papers

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