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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 1341–1350 of 1808 papers

Title	Date	Tasks	Status	Hype
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack	Jan 1, 2021	Adversarial AttackFew-Shot Learning	—Unverified	0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples	Jan 1, 2021	Adversarial AttackTransfer Learning	—Unverified	0
Adversarial Example Detection Using Latent Neighborhood Graph	Jan 1, 2021	Adversarial AttackGraph Attention	—Unverified	0
An Adversarial Attack via Feature Contributive Regions	Jan 1, 2021	Adversarial Attack	—Unverified	0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization	Dec 29, 2020	Adversarial AttackDepth Estimation	—Unverified	0
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition	Dec 21, 2020	Adversarial AttackAutomatic Speech Recognition	—Unverified	0
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring	Dec 21, 2020	Adversarial Attack	—Unverified	0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks	Dec 21, 2020	Adversarial AttackDimensionality Reduction	—Unverified	0
Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis	Dec 21, 2020	Adversarial AttackCryptanalysis	—Unverified	0
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks	Dec 17, 2020	Adversarial AttackDecision Making	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