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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 1391–1400 of 1808 papers

Title	Date	Tasks	Status	Hype
Accelerated Stochastic Gradient-free and Projection-free Methods	Jul 16, 2020	Adversarial Attack	CodeCode Available	0
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows	Jul 15, 2020	Adversarial Attack	CodeCode Available	1
Patch-wise Attack for Fooling Deep Neural Network	Jul 14, 2020	Adversarial AttackImage Classification	CodeCode Available	1
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack	Jul 14, 2020	Adversarial AttackCommon Sense Reasoning	—Unverified	0
Generating Adversarial Inputs Using A Black-box Differential Technique	Jul 10, 2020	Adversarial Attack	—Unverified	0
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection	Jul 10, 2020	Adversarial Attack	CodeCode Available	1
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs	Jul 8, 2020	Adversarial AttackDeep Learning	—Unverified	0
Black-box Adversarial Example Generation with Normalizing Flows	Jul 6, 2020	Adversarial Attack	CodeCode Available	1
On Data Augmentation and Adversarial Risk: An Empirical Analysis	Jul 6, 2020	Adversarial AttackData Augmentation	—Unverified	0
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain	Jul 5, 2020	Adversarial AttackBIG-bench Machine Learning	—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