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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 1611–1620 of 1808 papers

Title	Date	Tasks	Status	Hype
Natural Adversarial Examples	Jul 16, 2019	Adversarial AttackData Augmentation	CodeCode Available	1
Affine Disentangled GAN for Interpretable and Robust AV Perception	Jul 6, 2019	Adversarial AttackAutonomous Vehicles	—Unverified	0
Adversarial Attacks in Sound Event Classification	Jul 4, 2019	Adversarial AttackClassification	—Unverified	0
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack	Jul 3, 2019	Adversarial Attack	CodeCode Available	0
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency	Jul 1, 2019	Adversarial AttackClassification	CodeCode Available	0
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"	Jul 1, 2019	Adversarial AttackAdversarial Defense	—Unverified	0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks	Jun 17, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks	Jun 13, 2019	Adversarial AttackDeep Learning	—Unverified	0
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks	Jun 11, 2019	Adversarial Attack	CodeCode Available	0
Mimic and Fool: A Task Agnostic Adversarial Attack	Jun 11, 2019	Adversarial AttackImage Captioning	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