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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 571–580 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning	Oct 31, 2023	Adversarial Attackreinforcement-learning	CodeCode Available	0	5
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation	Mar 20, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks	Jul 12, 2021	Adversarial Attack	CodeCode Available	0	5
Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation	Sep 21, 2024	Adversarial AttackImage Generation	CodeCode Available	0	5
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training	Mar 2, 2021	Adversarial AttackAdversarial Robustness	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
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0	5
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	0	5
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA	Nov 5, 2020	Adversarial AttackCloud Computing	CodeCode Available	0	5
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	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