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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 21–30 of 1808 papers

Title	Date	Tasks	Status	Hype
BAE: BERT-based Adversarial Examples for Text Classification	Apr 4, 2020	Adversarial AttackAdversarial Text	CodeCode Available	2
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies	Mar 2, 2020	Adversarial Attack	CodeCode Available	2
A Little Fog for a Large Turn	Jan 16, 2020	Adversarial AttackAutonomous Navigation	CodeCode Available	2
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review	Sep 17, 2019	Adversarial Attack	CodeCode Available	2
Foolbox: A Python toolbox to benchmark the robustness of machine learning models	Jul 13, 2017	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	2
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models	Jul 8, 2025	Adversarial AttackDenoising	CodeCode Available	1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation	Jun 19, 2025	Adversarial AttackRobot Navigation	CodeCode Available	1
Learning Safety Constraints for Large Language Models	May 30, 2025	Adversarial Attack	CodeCode Available	1
3D Gaussian Splat Vulnerabilities	May 30, 2025	3DGSAdversarial Attack	CodeCode Available	1
SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents	May 29, 2025	Adversarial AttackLarge Language Model	CodeCode Available	1

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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