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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 261–270 of 1808 papers

Title	Date	Tasks	Status	Hype
Contextualized Perturbation for Textual Adversarial Attack	Sep 16, 2020	Adversarial AttackLanguage Modeling	CodeCode Available	1
Adversarial Attack on Large Scale Graph	Sep 8, 2020	Adversarial Attack	CodeCode Available	1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems	Aug 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks	Aug 9, 2020	Adversarial AttackImage Classification	CodeCode Available	1
Robust Deep Reinforcement Learning through Adversarial Loss	Aug 5, 2020	Adversarial AttackAtari Games	CodeCode Available	1
SimAug: Learning Robust Representations from Simulation for Trajectory Prediction	Aug 1, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	1
Sparse Adversarial Attack via Perturbation Factorization	Aug 1, 2020	Adversarial Attack	CodeCode Available	1
SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing	Aug 1, 2020	Adversarial AttackAttribute	CodeCode Available	1
Robust Tracking against Adversarial Attacks	Jul 20, 2020	Adversarial Attack	CodeCode Available	1
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering	Jul 19, 2020	Adversarial AttackData Augmentation	CodeCode Available	1

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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