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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 1361–1370 of 1808 papers

Title	Date	Tasks	Status	Hype
A New Perspective on Stabilizing GANs training: Direct Adversarial Training	Aug 19, 2020	Adversarial AttackImage Generation	CodeCode Available	0
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization	Aug 18, 2020	Adversarial Attack	—Unverified	0
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems	Aug 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Improving adversarial robustness of deep neural networks by using semantic information	Aug 18, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0
Model Robustness with Text Classification: Semantic-preserving adversarial attacks	Aug 12, 2020	Adversarial AttackClassification	—Unverified	0
FireBERT: Hardening BERT-based classifiers against adversarial attack	Aug 10, 2020	Adversarial Attack	CodeCode Available	0
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks	Aug 9, 2020	Adversarial AttackImage Classification	CodeCode Available	1
Visual Attack and Defense on Text	Aug 7, 2020	Adversarial Attack	—Unverified	0
Robust Deep Reinforcement Learning through Adversarial Loss	Aug 5, 2020	Adversarial AttackAtari Games	CodeCode Available	1
Stabilizing Deep Tomographic Reconstruction	Aug 4, 2020	Adversarial Attackcompressed sensing	—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