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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 341–350 of 1808 papers

Title	Date	Tasks	Status	Hype
AdvHaze: Adversarial Haze Attack	Apr 28, 2021	Adversarial Attack	—Unverified	0
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems	May 22, 2021	Adversarial Attack	—Unverified	0
Unsourced Adversarial CAPTCHA: A Bi-Phase Adversarial CAPTCHA Framework	Jun 12, 2025	Adversarial AttackDiversity	—Unverified	0
Adversarial Attacks and Dimensionality in Text Classifiers	Apr 3, 2024	Adversarial AttackAdversarial Defense	—Unverified	0
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems	Nov 20, 2023	Adversarial AttackFace Presentation Attack Detection	—Unverified	0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition	Oct 13, 2022	Adversarial AttackAttribute	—Unverified	0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off	May 21, 2025	Adversarial AttackAdversarial Robustness	—Unverified	0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning	Jul 14, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium	May 20, 2025	Adversarial Attack	—Unverified	0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks	Sep 19, 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