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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 351–360 of 1808 papers

Title	Date	Tasks	Status	Hype
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium	May 20, 2025	Adversarial Attack	—Unverified	0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks	Sep 19, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
Bias Field Poses a Threat to DNN-based X-Ray Recognition	Sep 19, 2020	Adversarial Attack	—Unverified	0
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy	Jun 4, 2023	Adversarial AttackAttribute	—Unverified	0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs	Jun 23, 2022	Adversarial AttackAutonomous Driving	—Unverified	0
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey	Jul 1, 2023	3D Point Cloud ClassificationAdversarial Attack	—Unverified	0
ADMM based Distributed State Observer Design under Sparse Sensor Attacks	Sep 13, 2022	Adversarial Attack	—Unverified	0
Bio-Inspired Adversarial Attack Against Deep Neural Networks	Jun 30, 2021	Adversarial AttackObject	—Unverified	0
Adversarial training with perturbation generator networks	Sep 25, 2019	Adversarial Attack	—Unverified	0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey	Mar 11, 2023	Adversarial AttackAdversarial Defense	—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