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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 731–740 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition	Oct 25, 2023	Adversarial AttackLicense Plate Detection	CodeCode Available	0
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval	Oct 23, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networks	Oct 22, 2023	Adversarial Attack	—Unverified	0
CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability	Oct 22, 2023	Adversarial Attack	CodeCode Available	0
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting	Oct 20, 2023	Adversarial AttackGrammatical Error Correction	CodeCode Available	0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation	Oct 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarial Training for Physics-Informed Neural Networks	Oct 18, 2023	Adversarial Attack	CodeCode Available	0
Black-box Targeted Adversarial Attack on Segment Anything (SAM)	Oct 16, 2023	Adversarial Attack	—Unverified	0
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks	Oct 16, 2023	Adversarial AttackFederated Learning	—Unverified	0
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization	Oct 16, 2023	Adversarial AttackSelf-Supervised Learning	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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