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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 1581–1590 of 1808 papers

Title	Date	Tasks	Status	Hype
AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation	Oct 6, 2019	Adversarial AttackSegmentation	—Unverified	0
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies	Oct 5, 2019	Adversarial Attack	—Unverified	0
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks	Oct 3, 2019	Adversarial AttackDecision Making	CodeCode Available	0
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions	Oct 3, 2019	Adversarial AttackTranslation	—Unverified	0
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack	Oct 1, 2019	Adversarial Attack	—Unverified	0
Role of Spatial Context in Adversarial Robustness for Object Detection	Sep 30, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Deep k-NN Defense against Clean-label Data Poisoning Attacks	Sep 29, 2019	Adversarial AttackData Poisoning	CodeCode Available	0
Towards Certified Defense for Unrestricted Adversarial Attacks	Sep 25, 2019	Adversarial Attack	—Unverified	0
Simple and Effective Stochastic Neural Networks	Sep 25, 2019	Adversarial AttackAdversarial Defense	—Unverified	0
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection	Sep 25, 2019	Adversarial Attack	—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