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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 1351–1360 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Retention Score: Quantifying Jailbreak Risks for Vision Language Models	Dec 23, 2024	Adversarial Attack	—Unverified	0	0
Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective	Jan 7, 2025	Adversarial AttackDeep Reinforcement Learning	—Unverified	0	0
Rethinking Adversarial Transferability from a Data Distribution Perspective	Sep 29, 2021	Adversarial Attack	—Unverified	0	0
Adversarial Attack with Pattern Replacement	Nov 25, 2019	Adversarial Attack	—Unverified	0	0
Rethinking Classifier and Adversarial Attack	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes	Sep 3, 2018	Adversarial AttackVocal Bursts Type Prediction	—Unverified	0	0
Transferable Adversarial Examples for Anchor Free Object Detection	Jun 3, 2021	Adversarial AttackObject	—Unverified	0	0
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness	May 28, 2021	Adversarial Attack	—Unverified	0	0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features	Oct 22, 2018	Adversarial AttackAutomatic Speech Recognition	—Unverified	0	0
Rethinking Textual Adversarial Defense for Pre-trained Language Models	Jul 21, 2022	Adversarial AttackAdversarial Defense	—Unverified	0	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