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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 1621–1630 of 1808 papers

Title	Date	Tasks	Status	Hype
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers	Jun 9, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Adversarial Attack Generation Empowered by Min-Max Optimization	Jun 9, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Robustness for Non-Parametric Classification: A Generic Attack and Defense	Jun 7, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
Efficient Project Gradient Descent for Ensemble Adversarial Attack	Jun 7, 2019	Adversarial Attack	CodeCode Available	0
Query-efficient Meta Attack to Deep Neural Networks	Jun 6, 2019	Adversarial AttackMeta-Learning	CodeCode Available	0
Should Adversarial Attacks Use Pixel p-Norm?	Jun 6, 2019	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Architecture Selection via the Trade-off Between Accuracy and Robustness	Jun 4, 2019	Adversarial Attack	—Unverified	0
Enhancing Transformation-based Defenses using a Distribution Classifier	Jun 1, 2019	Adversarial Attack	—Unverified	0
Improving VAEs' Robustness to Adversarial Attack	Jun 1, 2019	Adversarial Attack	—Unverified	0
Feature Space Perturbations Yield More Transferable Adversarial Examples	Jun 1, 2019	Adversarial Attack	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