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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 441–450 of 1808 papers

Title	Date	Tasks	Status	Hype
AutoAugment Input Transformation for Highly Transferable Targeted Attacks	Dec 21, 2023	Adversarial Attack	—Unverified	0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks	Sep 29, 2021	Adversarial AttackGPU	—Unverified	0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack	Mar 18, 2022	Adversarial AttackNetwork Pruning	—Unverified	0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method	Aug 19, 2023	Adversarial AttackAttribute	—Unverified	0
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information	Oct 19, 2021	Adversarial Attackglobal-optimization	—Unverified	0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem	Jan 1, 2021	Adversarial Attack	—Unverified	0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification	Jan 3, 2025	Adversarial AttackMeta-Learning	—Unverified	0
Adversarial Attacks and Defences for Skin Cancer Classification	Dec 13, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality	Mar 12, 2023	Adversarial Attack	—Unverified	0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective	Feb 19, 2023	Adversarial AttackBackdoor Attack	—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