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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 301–310 of 1808 papers

Title	Date	Tasks	Status	Hype
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness	Feb 7, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Attack on Community Detection by Hiding Individuals	Jan 22, 2020	Adversarial AttackCommunity Detection	CodeCode Available	1
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking	Jan 1, 2020	Adversarial AttackAutonomous Driving	CodeCode Available	1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack	Dec 22, 2019	Adversarial AttackAdversarial Text	CodeCode Available	1
Square Attack: a query-efficient black-box adversarial attack via random search	Nov 29, 2019	Adversarial Attack	CodeCode Available	1
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks	Aug 17, 2019	Adversarial Attack	CodeCode Available	1
Natural Adversarial Examples	Jul 16, 2019	Adversarial AttackData Augmentation	CodeCode Available	1
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers	Jun 9, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks	May 28, 2019	Adversarial AttackVocal Bursts Intensity Prediction	CodeCode Available	1
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking	May 27, 2019	Adversarial AttackAutonomous Driving	CodeCode Available	1

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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