SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1021–1030 of 1808 papers

TitleStatusHype
Learning Globally Optimized Language Structure via Adversarial Training—0
Learning Key Steps to Attack Deep Reinforcement Learning Agents—0
AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture—0
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning—0
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations—0
Learning to Defend by Learning to Attack—0
Learning to Defense by Learning to Attack—0
Learning to Detect Adversarial Examples Based on Class Scores—0
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization—0
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified