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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 1231–1240 of 1808 papers

Title	Date	Tasks	Status	Hype
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization	Aug 20, 2024	Adversarial AttackGraph Neural Network	—Unverified	0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection	Apr 29, 2021	Adversarial AttackGeneral Classification	—Unverified	0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment	Aug 19, 2022	Adversarial AttackGrammatical Error Correction	—Unverified	0
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments	Dec 11, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
Generalization to Mitigate Synonym Substitution Attacks	Nov 1, 2020	Adversarial Attack	—Unverified	0
Generating Adversarial Attacks in the Latent Space	Apr 10, 2023	Adversarial AttackGenerative Adversarial Network	—Unverified	0
Generating Adversarial Examples with an Optimized Quality	Jun 30, 2020	Adversarial AttackComputer Security	—Unverified	0
Generating Adversarial Inputs Using A Black-box Differential Technique	Jul 10, 2020	Adversarial Attack	—Unverified	0
Generating Black-Box Adversarial Examples in Sparse Domain	Jan 22, 2021	Adversarial Attackobject-detection	—Unverified	0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning	Dec 9, 2020	Adversarial Attack	—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