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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 1011–1020 of 1808 papers

Title	Date	Tasks	Status	Hype
Resilience of Named Entity Recognition Models under Adversarial Attack	Jul 1, 2022	Adversarial Attacknamed-entity-recognition	CodeCode Available	0
SHARP: Search-Based Adversarial Attack for Structured Prediction	Jul 1, 2022	Adversarial AttackDependency Parsing	—Unverified	0
Robustness of Explanation Methods for NLP Models	Jun 24, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
A Framework for Understanding Model Extraction Attack and Defense	Jun 23, 2022	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs	Jun 23, 2022	Adversarial AttackAutonomous Driving	—Unverified	0
AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture	Jun 22, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?	Jun 22, 2022	Adversarial AttackObject	—Unverified	0
Detecting Adversarial Examples in Batches -- a geometrical approach	Jun 17, 2022	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective	Jun 14, 2022	Adversarial Attackcounterfactual	—Unverified	0
Darknet Traffic Classification and Adversarial Attacks	Jun 12, 2022	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