SOTAVerified|Agents Browse Leaderboard About

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 1231–1240 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions	Apr 22, 2021	Adversarial Attack	—Unverified	0	0
Watertox: The Art of Simplicity in Universal Attacks A Cross-Model Framework for Robust Adversarial Generation	Dec 20, 2024	Adversarial AttackDiversity	—Unverified	0	0
Towards Sybil Resilience in Decentralized Learning	Jun 26, 2023	Adversarial AttackFederated Learning	—Unverified	0	0
Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors	Mar 6, 2024	Adversarial AttackAdversarial Defense	—Unverified	0	0
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions	Oct 3, 2019	Adversarial AttackTranslation	—Unverified	0	0
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense	Nov 16, 2021	Adversarial Attack	—Unverified	0	0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods	Apr 18, 2023	Adversarial AttackAutonomous Driving	—Unverified	0	0
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability	Apr 29, 2020	Adversarial Attack	—Unverified	0	0
Adversarial Imitation Attack	Mar 28, 2020	Adversarial Attack	—Unverified	0	0
Fooling the primate brain with minimal, targeted image manipulation	Nov 11, 2020	Adversarial AttackImage Manipulation	—Unverified	0	0

Show:10 25 50

← PrevPage 124 of 181Next →

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