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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 1601–1610 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies	Mar 26, 2022	Adversarial Attack	—Unverified	0	0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey	Mar 11, 2023	Adversarial AttackAdversarial Defense	—Unverified	0	0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers	Oct 4, 2022	Adversarial AttackContrastive Learning	—Unverified	0	0
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models	Feb 4, 2025	Adversarial AttackDiagnostic	—Unverified	0	0
CorrAttack: Black-box Adversarial Attack with Structured Search	Oct 3, 2020	Adversarial AttackBayesian Optimization	—Unverified	0	0
Correlation Analysis of Adversarial Attack in Time Series Classification	Aug 21, 2024	Adversarial AttackTime Series	—Unverified	0	0
Corruption Robust Offline Reinforcement Learning with Human Feedback	Feb 9, 2024	Adversarial Attackreinforcement-learning	—Unverified	0	0
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection	Mar 27, 2024	Adversarial AttackCo-Salient Object Detection	—Unverified	0	0
A Study for Universal Adversarial Attacks on Texture Recognition	Oct 4, 2020	Adversarial AttackDeep Learning	—Unverified	0	0
Should Adversarial Attacks Use Pixel p-Norm?	Jun 6, 2019	Adversarial AttackBIG-bench Machine Learning	—Unverified	0	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