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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 1131–1140 of 1808 papers

Title	Date	Tasks	Status	Hype
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks	Dec 6, 2021	Adversarial AttackData Poisoning	—Unverified	0
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts	Dec 1, 2021	Adversarial Attacktext-classification	CodeCode Available	0
Pyramid Adversarial Training Improves ViT Performance	Nov 30, 2021	Adversarial AttackData Augmentation	CodeCode Available	0
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack	Nov 29, 2021	Adversarial AttackDiagnostic	—Unverified	0
Adaptive Image Transformations for Transfer-based Adversarial Attack	Nov 27, 2021	Adversarial Attack	CodeCode Available	0
Adaptive Perturbation for Adversarial Attack	Nov 27, 2021	Adversarial Attack	—Unverified	0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network	Nov 25, 2021	Adversarial AttackDeblurring	—Unverified	0
Thundernna: a white box adversarial attack	Nov 24, 2021	Adversarial Attack	—Unverified	0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework	Nov 22, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Metamorphic Adversarial Detection Pipeline for Face Recognition Systems	Nov 22, 2021	Adversarial AttackFace Recognition	—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