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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 1661–1670 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
A Robust Likelihood Model for Novelty Detection	Jun 6, 2023	Adversarial AttackAnomaly Detection	—Unverified	0	0
ABIGX: A Unified Framework for eXplainable Fault Detection and Classification	Nov 9, 2023	Adversarial AttackClassification	—Unverified	0	0
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection	Jun 30, 2023	Adversarial AttackDeep Learning	—Unverified	0	0
Defense Against Explanation Manipulation	Nov 8, 2021	Adversarial AttackBIG-bench Machine Learning	—Unverified	0	0
Adversarial Attack on Sentiment Classification	Aug 1, 2019	Adversarial AttackClassification	—Unverified	0	0
Defense-guided Transferable Adversarial Attacks	Oct 22, 2020	Adversarial Attack	—Unverified	0	0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection	Jul 31, 2023	Adversarial AttackInformation Retrieval	—Unverified	0	0
AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles	Dec 31, 2023	Adversarial AttackAdversarial Defense	—Unverified	0	0
Defensive Quantization: When Efficiency Meets Robustness	Apr 17, 2019	Adversarial AttackCPU	—Unverified	0	0
SMART: Skeletal Motion Action Recognition aTtack	Nov 16, 2019	Action RecognitionAdversarial Attack	—Unverified	0	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