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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 461–470 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation	Nov 28, 2019	Adversarial AttackDomain Adaptation	CodeCode Available	0	5
LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial Attack	Aug 1, 2023	Adversarial AttackHard-label Attack	CodeCode Available	0	5
A Targeted Universal Attack on Graph Convolutional Network	Nov 29, 2020	Adversarial AttackGraph Neural Network	CodeCode Available	0	5
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts	Dec 1, 2021	Adversarial Attacktext-classification	CodeCode Available	0	5
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	0	5
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions	Apr 14, 2020	Adversarial AttackEmotion Classification	CodeCode Available	0	5
Fast Inference of Removal-Based Node Influence	Mar 13, 2024	Adversarial Attackcounterfactual	CodeCode Available	0	5
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	0	5
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0	5

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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