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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 591–600 of 1808 papers

Title	Date	Tasks	Status	Hype
Application of Adversarial Examples to Physical ECG Signals	Aug 20, 2021	Adversarial AttackECG Classification	—Unverified	0
Adversarial Examples for Model-Based Control: A Sensitivity Analysis	Jul 14, 2022	Adversarial AttackSensitivity	—Unverified	0
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space	Apr 17, 2020	Active LearningAdversarial Attack	—Unverified	0
Physical-World Optical Adversarial Attacks on 3D Face Recognition	May 26, 2022	Adversarial AttackFace Recognition	—Unverified	0
Adversarial Example Detection Using Latent Neighborhood Graph	Jan 1, 2021	Adversarial AttackGraph Attention	—Unverified	0
A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate	May 1, 2021	Adversarial Attack	—Unverified	0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning	Jun 8, 2023	Adversarial AttackIntrusion Detection	—Unverified	0
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique	Nov 14, 2019	Adversarial Attackimage-classification	—Unverified	0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization	Apr 11, 2022	Adversarial AttackObject	—Unverified	0
Adversarial Attack and Defense on Point Sets	Feb 28, 2019	Adversarial Attack	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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