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 1751–1775 of 1808 papers

Title	Date	Tasks	Status
Adversarial Metric Attack and Defense for Person Re-identification	Jan 30, 2019	Adversarial AttackBenchmarking	CodeCode Available
Deep generative models as an adversarial attack strategy for tabular machine learning	Sep 19, 2024	Adversarial Attack	CodeCode Available
DeepFool: a simple and accurate method to fool deep neural networks	Nov 14, 2015	Adversarial AttackGeneral Classification	CodeCode Available
A Theoretical View of Linear Backpropagation and Its Convergence	Dec 21, 2021	Adversarial Attack	CodeCode Available
Training Meta-Surrogate Model for Transferable Adversarial Attack	Sep 5, 2021	Adversarial Attackmodel	CodeCode Available
On Detecting Adversarial Perturbations	Feb 14, 2017	Adversarial AttackBinary Classification	CodeCode Available
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA	Nov 5, 2020	Adversarial AttackCloud Computing	CodeCode Available
Safety Verification of Deep Neural Networks	Oct 21, 2016	Adversarial AttackAdversarial Defense	CodeCode Available
A Targeted Universal Attack on Graph Convolutional Network	Nov 29, 2020	Adversarial AttackGraph Neural Network	CodeCode Available
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses	Jun 1, 2019	Adversarial AttackAdversarial Defense	CodeCode Available
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack	Jun 4, 2022	Adversarial Attack	CodeCode Available
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation	Jul 2, 2025	3DGSAdversarial Attack	CodeCode Available
Decorrelative Network Architecture for Robust Electrocardiogram Classification	Jul 19, 2022	Adversarial AttackAdversarial Defense	CodeCode Available
Decision-based Universal Adversarial Attack	Sep 15, 2020	Adversarial Attack	CodeCode Available
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation	Mar 9, 2023	Adversarial Attack	CodeCode Available
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation	Mar 20, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available
Data-Driven Subsampling in the Presence of an Adversarial Actor	Jan 7, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available
Trainwreck: A damaging adversarial attack on image classifiers	Nov 24, 2023	Adversarial AttackData Poisoning	CodeCode Available
Sample Attackability in Natural Language Adversarial Attacks	Jun 21, 2023	Adversarial Attack	CodeCode Available
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning	Feb 5, 2025	Adversarial AttackMulti-agent Reinforcement Learning	CodeCode Available
LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language Models	Mar 25, 2024	Adversarial AttackLanguage Modeling	CodeCode Available
On Robustness of Neural Ordinary Differential Equations	Oct 12, 2019	Adversarial Attack	CodeCode Available
Data-Driven Falsification of Cyber-Physical Systems	May 6, 2025	Adversarial AttackAutonomous Vehicles	CodeCode Available
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems	Mar 27, 2019	Adversarial AttackSentence	CodeCode Available
DAmageNet: A Universal Adversarial Dataset	Dec 16, 2019	Adversarial Attack	CodeCode Available

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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