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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 11–20 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints	Feb 25, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	2	5
Foolbox: A Python toolbox to benchmark the robustness of machine learning models	Jul 13, 2017	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	2	5
Ignore Previous Prompt: Attack Techniques For Language Models	Nov 17, 2022	Adversarial AttackAdversarial Text	CodeCode Available	2	5
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks	Jan 27, 2024	Adversarial AttackComputational Efficiency	CodeCode Available	2	5
Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment	May 27, 2025	Adversarial AttackClustering	CodeCode Available	2	5
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies	Mar 2, 2020	Adversarial Attack	CodeCode Available	2	5
Adversarial Attacks and Defenses on Text-to-Image Diffusion Models: A Survey	Jul 10, 2024	Adversarial AttackImage Generation	CodeCode Available	2	5
A Little Fog for a Large Turn	Jan 16, 2020	Adversarial AttackAutonomous Navigation	CodeCode Available	2	5
BAE: BERT-based Adversarial Examples for Text Classification	Apr 4, 2020	Adversarial AttackAdversarial Text	CodeCode Available	2	5
Backdoor Learning: A Survey	Jul 17, 2020	Adversarial AttackBackdoor Attack	CodeCode Available	2	5

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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