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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 1031–1040 of 1808 papers

Title	Date	Tasks	Status	Hype
Semantic Adversarial Attacks on Face Recognition through Significant Attributes	Jan 28, 2023	Adversarial AttackAttribute	—Unverified	0
Semantic Autoencoder and Its Potential Usage for Adversarial Attack	May 31, 2022	Adversarial Attack	—Unverified	0
Semantic Image Attack for Visual Model Diagnosis	Mar 23, 2023	Adversarial AttackAttribute	—Unverified	0
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm	Aug 25, 2022	Adversarial Attack	—Unverified	0
Model Robustness with Text Classification: Semantic-preserving adversarial attacks	Aug 12, 2020	Adversarial AttackClassification	—Unverified	0
SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models	Apr 16, 2025	Adversarial Attack	—Unverified	0
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images	Jul 13, 2024	Adversarial Attack	—Unverified	0
Sequential Attacks on Agents for Long-Term Adversarial Goals	May 31, 2018	Adversarial AttackReinforcement Learning	—Unverified	0
SHARP: Search-Based Adversarial Attack for Structured Prediction	Jul 1, 2022	Adversarial AttackDependency Parsing	—Unverified	0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness	Jun 1, 2019	Adversarial AttackAdversarial Robustness	—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