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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 1051–1060 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning	Jun 18, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0	0
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems	Mar 2, 2023	Adversarial AttackAutonomous Vehicles	—Unverified	0	0
Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification	Sep 10, 2019	Adversarial AttackGeneral Classification	—Unverified	0	0
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model	Mar 18, 2024	Adversarial AttackStyle Transfer	—Unverified	0	0
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise	Jun 5, 2024	Adversarial AttackQuantization	—Unverified	0	0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network	Nov 25, 2021	Adversarial AttackDeblurring	—Unverified	0	0
Towards Adversarially Robust Deep Image Denoising	Jan 12, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks	Jul 30, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Looking From the Future: Multi-order Iterations Can Enhance Adversarial Attack Transferability	Jul 2, 2024	Adversarial Attack	—Unverified	0	0
Improving VAEs' Robustness to Adversarial Attack	Jun 1, 2019	Adversarial Attack	—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