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 871–880 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey	Mar 11, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Feature Unlearning for Pre-trained GANs and VAEs	Mar 10, 2023	Adversarial Attack	—Unverified	0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems	Mar 10, 2023	Adversarial AttackAutomatic Speech Recognition	—Unverified	0
Do we need entire training data for adversarial training?	Mar 10, 2023	Adversarial AttackSelf-Driving Cars	—Unverified	0
Identification of Systematic Errors of Image Classifiers on Rare Subgroups	Mar 9, 2023	Adversarial AttackFairness	—Unverified	0
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation	Mar 9, 2023	Adversarial Attack	CodeCode Available	0
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration	Mar 7, 2023	Adversarial Attack	CodeCode Available	0
Adversarial Sampling for Fairness Testing in Deep Neural Network	Mar 6, 2023	Adversarial AttackFairness	—Unverified	0
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models	Mar 5, 2023	Adversarial AttackManagement	—Unverified	0
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems	Mar 2, 2023	Adversarial AttackAutonomous Vehicles	—Unverified	0

Show:10 25 50

← PrevPage 88 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