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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 1781–1790 of 1808 papers

Title	Date	Tasks	Status	Hype
On the Perils of Cascading Robust Classifiers	Jun 1, 2022	Adversarial Attack	CodeCode Available	0
An Analysis of Robustness of Non-Lipschitz Networks	Oct 13, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	0
Transferable 3D Adversarial Shape Completion using Diffusion Models	Jul 14, 2024	3D Point Cloud ClassificationAdversarial Attack	CodeCode Available	0
The Adversarial Attack and Detection under the Fisher Information Metric	Oct 9, 2018	Adversarial Attack	CodeCode Available	0
Adversarial Attacks on Gaussian Process Bandits	Oct 16, 2021	Adversarial AttackGaussian Processes	CodeCode Available	0
Curls & Whey: Boosting Black-Box Adversarial Attacks	Apr 2, 2019	Adversarial AttackDiversity	CodeCode Available	0
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks	Mar 27, 2019	Adversarial Attack	CodeCode Available	0
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection	Dec 5, 2023	3D Object DetectionAdversarial Attack	CodeCode Available	0
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack	Jan 18, 2024	Adversarial AttackStyle Transfer	CodeCode Available	0
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection	Nov 8, 2023	Active LearningAdversarial Attack	CodeCode Available	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