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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 271–280 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Immunization for Certifiable Robustness on Graphs	Jul 19, 2020	Adversarial AttackBilevel Optimization	CodeCode Available	1
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
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection	Jul 10, 2020	Adversarial Attack	CodeCode Available	1
Black-box Adversarial Example Generation with Normalizing Flows	Jul 6, 2020	Adversarial Attack	CodeCode Available	1
RayS: A Ray Searching Method for Hard-label Adversarial Attack	Jun 23, 2020	Adversarial AttackHard-label Attack	CodeCode Available	1
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers	Jun 19, 2020	Adversarial AttackLanguage Modeling	CodeCode Available	1
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution	Jun 15, 2020	Adversarial Attack	CodeCode Available	1
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1
Targeted Adversarial Perturbations for Monocular Depth Prediction	Jun 12, 2020	Adversarial AttackAdversarial Defense	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