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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 1031–1040 of 1808 papers

Title	Date	Tasks	Status	Hype
Unfooling Perturbation-Based Post Hoc Explainers	May 29, 2022	Adversarial AttackAnomaly Detection	CodeCode Available	0
Superclass Adversarial Attack	May 29, 2022	Adversarial AttackMulti-Label Classification	—Unverified	0
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks	May 29, 2022	Adversarial AttackClassification	—Unverified	0
Physical-World Optical Adversarial Attacks on 3D Face Recognition	May 26, 2022	Adversarial AttackFace Recognition	—Unverified	0
Adversarial Body Shape Search for Legged Robots	May 20, 2022	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning	May 19, 2022	Adversarial AttackMulti-agent Reinforcement Learning	—Unverified	0
Transferable Physical Attack against Object Detection with Separable Attention	May 19, 2022	Adversarial Attackobject-detection	—Unverified	0
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds	May 18, 2022	Adversarial AttackSteganalysis	—Unverified	0
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks	May 15, 2022	Adversarial AttackDomain Adaptation	—Unverified	0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images	May 9, 2022	Adversarial AttackAdversarial Attack Detection	—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