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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 1591–1600 of 1808 papers

Title	Date	Tasks	Status	Hype
Towards Evaluating the Robustness of Neural Networks	Aug 16, 2016	Adversarial Attack	CodeCode Available	0
Reducing DNN Properties to Enable Falsification with Adversarial Attacks	May 27, 2021	Adversarial AttackAutonomous Vehicles	CodeCode Available	0
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only	Oct 10, 2018	Adversarial AttackQuantization	CodeCode Available	0
AdvGPS: Adversarial GPS for Multi-Agent Perception Attack	Jan 30, 2024	Adversarial Attackobject-detection	CodeCode Available	0
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch	Feb 20, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors	Jun 11, 2020	Adversarial AttackMedical Image Analysis	CodeCode Available	0
A black-box adversarial attack for poisoning clustering	Sep 9, 2020	Adversarial AttackClustering	CodeCode Available	0
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization	Dec 9, 2022	Adversarial Attack	CodeCode Available	0
Statistical inference for individual fairness	Mar 30, 2021	Adversarial AttackFairness	CodeCode Available	0
Robust Smart Home Face Recognition under Starving Federated Data	Nov 10, 2022	Adversarial AttackFace Recognition	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