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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 961–970 of 1808 papers

Title	Date	Tasks	Status	Hype
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks	Oct 6, 2022	Adversarial AttackDiversity	—Unverified	0
Jitter Does Matter: Adapting Gaze Estimation to New Domains	Oct 5, 2022	Adversarial AttackContrastive Learning	—Unverified	0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers	Oct 4, 2022	Adversarial AttackContrastive Learning	—Unverified	0
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework	Oct 4, 2022	Adversarial AttackClustering	CodeCode Available	0
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks	Oct 3, 2022	Adversarial Attack	—Unverified	0
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack	Oct 1, 2022	Adversarial AttackDomain Adaptation	—Unverified	0
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack	Oct 1, 2022	Adversarial Attack	—Unverified	0
A Survey on Physical Adversarial Attack in Computer Vision	Sep 28, 2022	Adversarial Attackobject-detection	—Unverified	0
Activation Learning by Local Competitions	Sep 26, 2022	Adversarial AttackAnomaly Detection	—Unverified	0
Fair Robust Active Learning by Joint Inconsistency	Sep 22, 2022	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