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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 471–480 of 1808 papers

Title	Date	Tasks	Status	Hype
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors	Oct 29, 2023	Adversarial Attack	—Unverified	0
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
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models	Oct 23, 2023	Adversarial AttackBlocking	CodeCode Available	1
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
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks	Oct 16, 2023	Adversarial AttackFederated 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