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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 1641–1650 of 1808 papers

Title	Date	Tasks	Status	Hype
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation	Nov 28, 2019	Adversarial AttackDomain Adaptation	CodeCode Available	0
LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial Attack	Aug 1, 2023	Adversarial AttackHard-label Attack	CodeCode Available	0
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks	Jun 11, 2019	Adversarial Attack	CodeCode Available	0
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	May 31, 2019	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0
Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation	May 5, 2025	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach	Jun 6, 2023	Adversarial AttackColor Image Denoising	CodeCode Available	0
Local Aggressive Adversarial Attacks on 3D Point Cloud	May 19, 2021	Adversarial AttackImage to 3D	CodeCode Available	0
Adversarial Purification of Information Masking	Nov 26, 2023	Adversarial AttackAdversarial Purification	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
Disrupting Deep Uncertainty Estimation Without Harming Accuracy	Oct 26, 2021	Adversarial Attack	CodeCode Available	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