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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 1041–1050 of 1808 papers

Title	Date	Tasks	Status	Hype
Should Adversarial Attacks Use Pixel p-Norm?	Jun 6, 2019	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
SIGL: Securing Software Installations Through Deep Graph Learning	Aug 26, 2020	Adversarial AttackDecoder	—Unverified	0
Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack	Oct 2, 2024	Adversarial Attack	—Unverified	0
Similarity of Neural Architectures using Adversarial Attack Transferability	Oct 20, 2022	Adversarial AttackDiversity	—Unverified	0
Simple and Effective Stochastic Neural Networks	Sep 25, 2019	Adversarial AttackAdversarial Defense	—Unverified	0
Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis	Aug 6, 2024	Adversarial Attackfeature selection	—Unverified	0
SMART: Skeletal Motion Action Recognition aTtack	Nov 16, 2019	Action RecognitionAdversarial Attack	—Unverified	0
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization	Dec 8, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Snowball Adversarial Attack on Traffic Sign Classification	Feb 27, 2025	Adversarial AttackClassification	—Unverified	0
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method	Mar 18, 2020	Adversarial 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