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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 1771–1780 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning	May 14, 2020	Adversarial AttackDeep Reinforcement Learning	—Unverified	0	0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation	May 25, 2021	Adversarial AttackData Augmentation	—Unverified	0	0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples	Aug 20, 2018	Adversarial AttackMetric Learning	—Unverified	0	0
Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation	Nov 23, 2024	Adversarial AttackFace Recognition	—Unverified	0	0
Enhancing the Transferability via Feature-Momentum Adversarial Attack	Apr 22, 2022	Adversarial Attack	—Unverified	0	0
Enhancing TinyML Security: Study of Adversarial Attack Transferability	Jul 16, 2024	Adversarial AttackEdge-computing	—Unverified	0	0
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks	Feb 28, 2024	Adversarial AttackAdversarial Defense	—Unverified	0	0
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing	Aug 22, 2024	Adversarial AttackComputational Efficiency	—Unverified	0	0
Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier	May 1, 2020	Adversarial Attack	—Unverified	0	0
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling	Mar 11, 2021	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