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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 761–770 of 1808 papers

Title	Date	Tasks	Status	Hype
A Survey on Physical Adversarial Attack in Computer Vision	Sep 28, 2022	Adversarial Attackobject-detection	—Unverified	0
Activation Learning by Local Competitions	Sep 26, 2022	Adversarial AttackAnomaly Detection	—Unverified	0
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning	Sep 24, 2022	Adversarial Attack	CodeCode Available	1
Fair Robust Active Learning by Joint Inconsistency	Sep 22, 2022	Active LearningAdversarial Attack	—Unverified	0
Adversarial Color Projection: A Projector-based Physical Attack to DNNs	Sep 19, 2022	Adversarial Attack	—Unverified	0
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction	Sep 19, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models	Sep 17, 2022	Adversarial AttackAutomatic Speech Recognition	CodeCode Available	0
Robust Constrained Reinforcement Learning	Sep 14, 2022	Adversarial Attackreinforcement-learning	—Unverified	0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack	Sep 14, 2022	3D ClassificationAdversarial Attack	CodeCode Available	0
TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack	Sep 14, 2022	Adversarial Attackglobal-optimization	CodeCode Available	1

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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