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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 1511–1520 of 1808 papers

Title	Date	Tasks	Status	Hype
Identifying Adversarially Attackable and Robust Samples	Jan 30, 2023	Active LearningAdversarial Attack	CodeCode Available	0
Towards Analyzing Semantic Robustness of Deep Neural Networks	Apr 9, 2019	Adversarial AttackAutonomous Driving	CodeCode Available	0
Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning	Jan 25, 2021	Adversarial AttackDNN Testing	CodeCode Available	0
Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF Infeasible	Jul 10, 2025	Adversarial AttackBenchmarking	CodeCode Available	0
Simple and Efficient Partial Graph Adversarial Attack: A New Perspective	Aug 15, 2023	Adversarial Attack	CodeCode Available	0
Functional Adversarial Attacks	May 29, 2019	Adversarial Attack	CodeCode Available	0
Efficient Project Gradient Descent for Ensemble Adversarial Attack	Jun 7, 2019	Adversarial Attack	CodeCode Available	0
Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective	Apr 30, 2024	Adversarial Attack	CodeCode Available	0
An Evasion Attack against Stacked Capsule Autoencoder	Oct 14, 2020	Adversarial Attackimage-classification	CodeCode Available	0
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems	Jul 12, 2023	Adversarial AttackDeep Learning	CodeCode Available	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