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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 1051–1060 of 1808 papers

Title	Date	Tasks	Status	Hype
Empirical Study of the Decision Region and Robustness in Deep Neural Networks	Sep 29, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Neural Networks Playing Dough: Investigating Deep Cognition With a Gradient-Based Adversarial Attack	Sep 29, 2021	Adversarial Attack	—Unverified	0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks	Sep 29, 2021	Adversarial AttackGPU	—Unverified	0
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs	Sep 29, 2021	Adversarial AttackObject Recognition	—Unverified	0
Fooling Adversarial Training with Induction Noise	Sep 29, 2021	Adversarial Attack	—Unverified	0
One for Many: an Instagram inspired black-box adversarial attack	Sep 29, 2021	Adversarial Attack	—Unverified	0
Rethinking Adversarial Transferability from a Data Distribution Perspective	Sep 29, 2021	Adversarial Attack	—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

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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