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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 1241–1250 of 1808 papers

Title	Date	Tasks	Status	Hype
Generating Semantic Adversarial Examples via Feature Manipulation	Jan 6, 2020	Adversarial AttackDisentanglement	—Unverified	0
Generating Semantically Valid Adversarial Questions for TableQA	May 26, 2020	Adversarial AttackQuestion Answering	—Unverified	0
Generating Unrestricted Adversarial Examples via Three Parameters	Mar 13, 2021	Adversarial Attack	—Unverified	0
Generating Valid and Natural Adversarial Examples with Large Language Models	Nov 20, 2023	Adversarial Attackvalid	—Unverified	0
Generating Watermarked Adversarial Texts	Oct 25, 2021	Adversarial AttackAdversarial Text	—Unverified	0
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing	Feb 16, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification	Oct 26, 2024	Adversarial Attack	—Unverified	0
Global Robustness Verification Networks	Jun 8, 2020	Adversarial Attack	—Unverified	0
Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification	Sep 17, 2024	Adversarial AttackBinarization	—Unverified	0
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world	Mar 9, 2020	Adversarial AttackDiversity	—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