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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 1761–1770 of 1808 papers

Title	Date	Tasks	Status	Hype
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack	Jun 4, 2022	Adversarial Attack	CodeCode Available	0
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation	Jul 2, 2025	3DGSAdversarial Attack	CodeCode Available	0
Decorrelative Network Architecture for Robust Electrocardiogram Classification	Jul 19, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	0
Decision-based Universal Adversarial Attack	Sep 15, 2020	Adversarial Attack	CodeCode Available	0
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation	Mar 9, 2023	Adversarial Attack	CodeCode Available	0
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation	Mar 20, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Data-Driven Subsampling in the Presence of an Adversarial Actor	Jan 7, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Trainwreck: A damaging adversarial attack on image classifiers	Nov 24, 2023	Adversarial AttackData Poisoning	CodeCode Available	0
Sample Attackability in Natural Language Adversarial Attacks	Jun 21, 2023	Adversarial Attack	CodeCode Available	0
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning	Feb 5, 2025	Adversarial AttackMulti-agent Reinforcement Learning	CodeCode Available	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