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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 881–890 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Attack on Skeleton-based Human Action Recognition	Sep 14, 2019	Action RecognitionAdversarial Attack	—Unverified	0	0
Generalization to Mitigate Synonym Substitution Attacks	Nov 1, 2020	Adversarial Attack	—Unverified	0	0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization	May 9, 2024	Adversarial AttackDeep Learning	—Unverified	0	0
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments	Dec 11, 2022	Adversarial AttackAdversarial Defense	—Unverified	0	0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment	Aug 19, 2022	Adversarial AttackGrammatical Error Correction	—Unverified	0	0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds	Jan 15, 2021	Adversarial AttackAdversarial Defense	—Unverified	0	0
CE-based white-box adversarial attacks will not work using super-fitting	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework	Nov 22, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks	Jan 21, 2025	Adversarial AttackAll	—Unverified	0	0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs	Nov 18, 2020	Adversarial Attack	—Unverified	0	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