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 1576–1600 of 1808 papers

Title	Date	Tasks	Status
Uncertainty Estimation of Transformer Predictions for Misclassification Detection	May 1, 2022	Active LearningAdversarial Attack	CodeCode Available
Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models	Apr 19, 2021	Adversarial AttackManagement	CodeCode Available
Real-world adversarial attack on MTCNN face detection system	Oct 14, 2019	Adversarial Attack	CodeCode Available
Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients	Mar 5, 2025	Adversarial Attack	CodeCode Available
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis	Jul 16, 2024	Adversarial Attack	CodeCode Available
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization	Sep 23, 2018	Adversarial AttackAdversarial Defense	CodeCode Available
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex	May 29, 2019	Adversarial AttackBayesian Inference	CodeCode Available
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack	Mar 5, 2021	Adversarial AttackDiagnostic	CodeCode Available
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network	Dec 17, 2021	3D Classification3D Semantic Segmentation	CodeCode Available
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics	Mar 9, 2024	Adversarial Attack	CodeCode Available
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack	Jun 1, 2021	Adversarial AttackRepresentation Learning	CodeCode Available
Dynamic Adversarial Attacks on Autonomous Driving Systems	Dec 10, 2023	Adversarial AttackAutonomous Driving	CodeCode Available
Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?	Feb 6, 2019	Adversarial AttackAdversarial Defense	CodeCode Available
Bitstream Collisions in Neural Image Compression via Adversarial Perturbations	Mar 25, 2025	Adversarial AttackImage Compression	CodeCode Available
Stabilized Medical Image Attacks	Mar 9, 2021	Adversarial AttackMedical Diagnosis	CodeCode Available
Towards Evaluating the Robustness of Neural Networks	Aug 16, 2016	Adversarial Attack	CodeCode Available
Reducing DNN Properties to Enable Falsification with Adversarial Attacks	May 27, 2021	Adversarial AttackAutonomous Vehicles	CodeCode Available
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only	Oct 10, 2018	Adversarial AttackQuantization	CodeCode Available
AdvGPS: Adversarial GPS for Multi-Agent Perception Attack	Jan 30, 2024	Adversarial Attackobject-detection	CodeCode Available
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch	Feb 20, 2019	Adversarial AttackAdversarial Defense	CodeCode Available
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors	Jun 11, 2020	Adversarial AttackMedical Image Analysis	CodeCode Available
A black-box adversarial attack for poisoning clustering	Sep 9, 2020	Adversarial AttackClustering	CodeCode Available
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization	Dec 9, 2022	Adversarial Attack	CodeCode Available
Statistical inference for individual fairness	Mar 30, 2021	Adversarial AttackFairness	CodeCode Available
Robust Smart Home Face Recognition under Starving Federated Data	Nov 10, 2022	Adversarial AttackFace Recognition	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