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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 951–960 of 1808 papers

Title	Date	Tasks	Status	Hype
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack	Sep 28, 2017	Adversarial Attack	—Unverified	0
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation	Nov 15, 2022	Adversarial Attack	—Unverified	0
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping	Apr 26, 2022	Adversarial AttackFace Reconstruction	—Unverified	0
Retention Score: Quantifying Jailbreak Risks for Vision Language Models	Dec 23, 2024	Adversarial Attack	—Unverified	0
Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective	Jan 7, 2025	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
Rethinking Adversarial Transferability from a Data Distribution Perspective	Sep 29, 2021	Adversarial Attack	—Unverified	0
Rethinking Classifier and Adversarial Attack	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness	May 28, 2021	Adversarial Attack	—Unverified	0
Rethinking Textual Adversarial Defense for Pre-trained Language Models	Jul 21, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack	Aug 10, 2024	Adversarial AttackDenoising	—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