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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 1781–1790 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms	Sep 15, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0	0
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter	Aug 1, 2021	Adversarial AttackNMT	—Unverified	0	0
ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models	Dec 22, 2024	Adversarial AttackFace Recognition	—Unverified	0	0
Adversarial Attack by Limited Point Cloud Surface Modifications	Oct 7, 2021	Adversarial AttackPoint Cloud Classification	—Unverified	0	0
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization	Oct 16, 2023	Adversarial AttackSelf-Supervised Learning	—Unverified	0	0
EVALOOP: Assessing LLM Robustness in Programming from a Self-consistency Perspective	May 18, 2025	Adversarial AttackCode Generation	—Unverified	0	0
Stochastic Variance Reduced Ensemble Adversarial Attack	Sep 29, 2021	Adversarial Attack	—Unverified	0	0
Evaluating Adversarial Robustness on Document Image Classification	Apr 24, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Adversarial Attack Based on Prediction-Correction	Jun 2, 2023	Adversarial AttackPrediction	—Unverified	0	0
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication	Oct 3, 2021	Adversarial AttackBIG-bench Machine Learning	—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