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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 111–120 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation Models	Jul 5, 2024	Adversarial AttackAutomatic Speech Recognition	CodeCode Available	1
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting	Dec 11, 2024	Adversarial AttackTime Series	CodeCode Available	1
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks	Feb 4, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Deep Variational Information Bottleneck	Dec 1, 2016	Adversarial Attack	CodeCode Available	1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition	May 7, 2021	Adversarial AttackFace Generation	CodeCode Available	1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape	Apr 24, 2024	Adversarial AttackFace Swapping	CodeCode Available	1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation	Jun 19, 2025	Adversarial AttackRobot Navigation	CodeCode Available	1
Adversarial Ranking Attack and Defense	Feb 26, 2020	Adversarial AttackImage Retrieval	CodeCode Available	1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model	Nov 21, 2021	Adversarial AttackFace Recognition	CodeCode Available	1

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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