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 581–590 of 1808 papers

Title	Date	Tasks	Status	Hype
Graph-based methods coupled with specific distributional distances for adversarial attack detection	May 31, 2023	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework	May 29, 2023	Adversarial Attack	CodeCode Available	0
Adversarial Attack On Yolov5 For Traffic And Road Sign Detection	May 27, 2023	Adversarial Attackobject-detection	CodeCode Available	1
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making	May 27, 2023	Adversarial AttackDecision Making	CodeCode Available	0
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation	May 25, 2023	Adversarial AttackRain Removal	—Unverified	0
Another Dead End for Morphological Tags? Perturbed Inputs and Parsing	May 24, 2023	Adversarial Attack	CodeCode Available	0
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning	May 23, 2023	Adversarial AttackContinual Learning	—Unverified	0
Attribute-Guided Encryption with Facial Texture Masking	May 22, 2023	Adversarial AttackAttribute	—Unverified	0
Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space	May 22, 2023	Adversarial AttackSSIM	—Unverified	0
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks	May 22, 2023	Adversarial AttackAutonomous Driving	CodeCode Available	1

Show:10 25 50

← PrevPage 59 of 181Next →

All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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