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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 1191–1200 of 1808 papers

Title	Date	Tasks	Status	Hype
Pixab-CAM: Attend Pixel, not Channel	Sep 29, 2021	Adversarial Attack	—Unverified	0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References	Sep 29, 2021	Adversarial AttackImage Augmentation	—Unverified	0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors	Sep 27, 2021	Adversarial AttackClustering	CodeCode Available	0
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial Attack	Sep 23, 2021	Adversarial AttackDecision Making	CodeCode Available	0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers	Sep 22, 2021	Active LearningAdversarial Attack	—Unverified	0
Robust Physical-World Attacks on Face Recognition	Sep 20, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Universal Adversarial Attack on Deep Learning Based Prognostics	Sep 15, 2021	Adversarial AttackDeep Learning	—Unverified	0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder	Sep 14, 2021	Adversarial AttackContrastive Learning	—Unverified	0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems	Sep 13, 2021	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator	Sep 13, 2021	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