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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 1301–1310 of 1808 papers

Title	Date	Tasks	Status	Hype
Universal Adversarial Perturbations and Image Spam Classifiers	Mar 7, 2021	Adversarial AttackDeep Learning	—Unverified	0
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack	Mar 5, 2021	Adversarial AttackDiagnostic	CodeCode Available	0
A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models	Mar 3, 2021	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training	Mar 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	0
A Brief Survey on Deep Learning Based Data Hiding	Mar 2, 2021	Adversarial AttackDeep Learning	—Unverified	0
Model-Agnostic Defense for Lane Detection against Adversarial Attack	Mar 1, 2021	Adversarial AttackAutonomous Driving	CodeCode Available	0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding	Feb 24, 2021	Adversarial AttackClassification	—Unverified	0
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks	Feb 19, 2021	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification	Feb 15, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Certifiably Robust Variational Autoencoders	Feb 15, 2021	Adversarial AttackDecoder	—Unverified	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