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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 581–590 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	0	5
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	0	5
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	0	5
Data-Driven Subsampling in the Presence of an Adversarial Actor	Jan 7, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
FDA: Feature Disruptive Attack	Sep 10, 2019	Adversarial Attackimage-classification	CodeCode Available	0	5
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients	Aug 8, 2023	Adversarial AttackFederated Learning	CodeCode Available	0	5
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques	Dec 3, 2020	Adversarial AttackData Augmentation	CodeCode Available	0	5
Data-Driven Falsification of Cyber-Physical Systems	May 6, 2025	Adversarial AttackAutonomous Vehicles	CodeCode Available	0	5
Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models	Aug 15, 2024	Adversarial AttackLanguage Modeling	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