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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 1261–1270 of 1808 papers

Title	Date	Tasks	Status	Hype
Local Aggressive Adversarial Attacks on 3D Point Cloud	May 19, 2021	Adversarial AttackImage to 3D	CodeCode Available	0
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds	May 11, 2021	Adversarial AttackBackdoor Attack	—Unverified	0
Automated Decision-based Adversarial Attacks	May 9, 2021	Adversarial AttackProgram Synthesis	—Unverified	0
Self-Supervised Adversarial Example Detection by Disentangled Representation	May 8, 2021	Adversarial Attack	—Unverified	0
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning	May 5, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	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
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection	Apr 29, 2021	Adversarial AttackGeneral Classification	—Unverified	0
AdvHaze: Adversarial Haze Attack	Apr 28, 2021	Adversarial Attack	—Unverified	0
Delving into Data: Effectively Substitute Training for Black-box Attack	Apr 26, 2021	Adversarial Attack	—Unverified	0
Influence Based Defense Against Data Poisoning Attacks in Online Learning	Apr 24, 2021	Adversarial AttackData Poisoning	—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