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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 1651–1660 of 1808 papers

Title	Date	Tasks	Status	Hype
Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain	May 9, 2019	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
CharBot: A Simple and Effective Method for Evading DGA Classifiers	May 3, 2019	Adversarial Attack	—Unverified	0
Weight Map Layer for Noise and Adversarial Attack Robustness	May 2, 2019	Adversarial Attack	—Unverified	0
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm	May 1, 2019	Adversarial AttackDiversity	—Unverified	0
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks	May 1, 2019	Adversarial Attack	CodeCode Available	0
CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild	May 1, 2019	Adversarial AttackObject	CodeCode Available	0
NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK	May 1, 2019	Adversarial Attack	—Unverified	0
Second-Order Adversarial Attack and Certifiable Robustness	May 1, 2019	Adversarial Attack	—Unverified	0
Adversarial Training for Free!	Apr 29, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping	Apr 23, 2019	Adversarial AttackAutonomous Vehicles	—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