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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 371–380 of 1808 papers

Title	Date	Tasks	Status	Hype
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm	Mar 8, 2024	Adversarial AttackAutonomous Navigation	—Unverified	0
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds	Mar 8, 2024	3D Point Cloud ClassificationAdversarial Attack	CodeCode Available	1
Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors	Mar 6, 2024	Adversarial AttackAdversarial Defense	—Unverified	0
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models	Mar 4, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	2
SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters	Mar 2, 2024	Adversarial Attack	—Unverified	0
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey	Mar 1, 2024	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification	Feb 29, 2024	Adversarial AttackClassification	—Unverified	0
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks	Feb 28, 2024	Adversarial AttackAdversarial Defense	—Unverified	0
Extreme Miscalibration and the Illusion of Adversarial Robustness	Feb 27, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0
Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification	Feb 27, 2024	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