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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 1251–1260 of 1808 papers

Title	Date	Tasks	Status	Hype
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm	Jun 10, 2021	Adversarial Attack	CodeCode Available	0
Transferable Adversarial Examples for Anchor Free Object Detection	Jun 3, 2021	Adversarial AttackObject	—Unverified	0
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack	Jun 3, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack	Jun 1, 2021	Adversarial AttackRepresentation Learning	CodeCode Available	0
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice	May 30, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	0
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness	May 28, 2021	Adversarial Attack	—Unverified	0
Reducing DNN Properties to Enable Falsification with Adversarial Attacks	May 27, 2021	Adversarial AttackAutonomous Vehicles	CodeCode Available	0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge	May 26, 2021	Adversarial AttackGraph Embedding	—Unverified	0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation	May 25, 2021	Adversarial AttackData Augmentation	—Unverified	0
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems	May 22, 2021	Adversarial Attack	—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