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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 801–810 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey	Jul 1, 2023	3D Point Cloud ClassificationAdversarial Attack	—Unverified	0	0
ADMM based Distributed State Observer Design under Sparse Sensor Attacks	Sep 13, 2022	Adversarial Attack	—Unverified	0	0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks	Sep 19, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Boosting Adversarial Transferability through Enhanced Momentum	Mar 19, 2021	Adversarial Attack	—Unverified	0	0
Boosting Adversarial Transferability of MLP-Mixer	Apr 26, 2022	Adversarial Attack	—Unverified	0	0
Adversarial training with perturbation generator networks	Sep 25, 2019	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
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring	Dec 21, 2020	Adversarial Attack	—Unverified	0	0
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples	Feb 5, 2018	Adversarial Attack	—Unverified	0	0
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness	Apr 22, 2019	Adversarial Attack	—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