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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 1071–1080 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
MathAttack: Attacking Large Language Models Towards Math Solving Ability	Sep 4, 2023	Adversarial AttackGSM8K	—Unverified	0	0
Maximal Jacobian-based Saliency Map Attack	Aug 23, 2018	Adversarial AttackClassification	—Unverified	0	0
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems	Nov 20, 2023	Adversarial AttackFace Presentation Attack Detection	—Unverified	0	0
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare	Dec 11, 2021	Adversarial AttackPosition	—Unverified	0	0
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack	Nov 29, 2021	Adversarial AttackDiagnostic	—Unverified	0	0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning	Jul 14, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Vulnerability of Appearance-based Gaze Estimation	Mar 24, 2021	Adversarial AttackGaze Estimation	—Unverified	0	0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack	Jan 1, 2021	Adversarial AttackFew-Shot Learning	—Unverified	0	0
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium	May 20, 2025	Adversarial Attack	—Unverified	0	0
Metamorphic Adversarial Detection Pipeline for Face Recognition Systems	Nov 22, 2021	Adversarial AttackFace Recognition	—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