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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 51–60 of 1808 papers

Title	Date	Tasks	Status	Hype
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models	May 16, 2025	Adversarial AttackAdversarial Defense	CodeCode Available	1
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting	Dec 11, 2024	Adversarial AttackTime Series	CodeCode Available	1
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Immunization for Certifiable Robustness on Graphs	Jul 19, 2020	Adversarial AttackBilevel Optimization	CodeCode Available	1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain	Aug 19, 2021	Adversarial AttackData Augmentation	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
An Efficient Adversarial Attack for Tree Ensembles	Oct 22, 2020	Adversarial Attackvalid	CodeCode Available	1
Fooling the Image Dehazing Models by First Order Gradient	Mar 30, 2023	Adversarial AttackImage Dehazing	CodeCode Available	1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking	Mar 3, 2022	Adversarial AttackObject Tracking	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