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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 231–240 of 1808 papers

Title	Date	Tasks	Status	Hype
Patch-wise++ Perturbation for Adversarial Targeted Attacks	Dec 31, 2020	Adversarial Attack	CodeCode Available	1
Sparse Adversarial Attack to Object Detection	Dec 26, 2020	Adversarial AttackObject	CodeCode Available	1
Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification	Dec 21, 2020	Adversarial AttackBackdoor Attack	CodeCode Available	1
Efficient Training of Robust Decision Trees Against Adversarial Examples	Dec 18, 2020	Adversarial Attack	CodeCode Available	1
Disentangled Information Bottleneck	Dec 14, 2020	Adversarial AttackOut-of-Distribution Detection	CodeCode Available	1
SPAA: Stealthy Projector-based Adversarial Attacks on Deep Image Classifiers	Dec 10, 2020	Adversarial Attack	CodeCode Available	1
Geometric Adversarial Attacks and Defenses on 3D Point Clouds	Dec 10, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	1
Composite Adversarial Attacks	Dec 10, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness in Object Detection	Dec 8, 2020	2D Object DetectionAdversarial Attack	CodeCode Available	1
Adversarial Learning for Robust Deep Clustering	Dec 1, 2020	Adversarial AttackClustering	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