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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 1241–1250 of 1808 papers

Title	Date	Tasks	Status	Hype
Attack Transferability Characterization for Adversarially Robust Multi-label Classification	Jun 29, 2021	Adversarial AttackClassification	CodeCode Available	0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack	Jun 28, 2021	Adversarial AttackFeature Importance	—Unverified	0
Attack to Fool and Explain Deep Networks	Jun 20, 2021	Adversarial AttackImage Generation	—Unverified	0
Limited Budget Adversarial Attack Against Online Image Stream	Jun 18, 2021	Adversarial Attack	—Unverified	0
Light Lies: Optical Adversarial Attack	Jun 18, 2021	Adversarial AttackClassification	—Unverified	0
Is It Time to Redefine the Classification Task for Deep Learning Systems?	Jun 18, 2021	Adversarial AttackClassification	—Unverified	0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents	Jun 18, 2021	Adversarial Attackcontinuous-control	—Unverified	0
Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions	Jun 18, 2021	Adversarial Attack	—Unverified	0
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology	Jun 14, 2021	Adversarial AttackDiagnostic	—Unverified	0
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models	Jun 13, 2021	Adversarial AttackNatural Language Understanding	—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