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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 1411–1420 of 1808 papers

Title	Date	Tasks	Status	Hype
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations	Sep 19, 2020	Adversarial AttackNatural Language Inference	—Unverified	0
Adversarial Rain Attack and Defensive Deraining for DNN Perception	Sep 19, 2020	Adversarial AttackAutonomous Driving	—Unverified	0
MultAV: Multiplicative Adversarial Videos	Sep 17, 2020	Adversarial AttackVideo Recognition	—Unverified	0
Label Smoothing and Adversarial Robustness	Sep 17, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Decision-based Universal Adversarial Attack	Sep 15, 2020	Adversarial Attack	CodeCode Available	0
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks	Sep 15, 2020	Adversarial Attack	CodeCode Available	0
Input Hessian Regularization of Neural Networks	Sep 14, 2020	Adversarial Attack	—Unverified	0
A black-box adversarial attack for poisoning clustering	Sep 9, 2020	Adversarial AttackClustering	CodeCode Available	0
Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method	Sep 7, 2020	Adversarial AttackImage Reconstruction	—Unverified	0
Adversarially Robust Neural Architectures	Sep 2, 2020	Adversarial AttackAdversarial Robustness	—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