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 121–130 of 1808 papers

TitleStatusHype
HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate CampaignsCode1
The Relationship Between Network Similarity and Transferability of Adversarial Attacks—0
GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm—0
Device-aware Optical Adversarial Attack for a Portable Projector-camera System—0
Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
Robustness of Selected Learning Models under Label-Flipping Attack—0
Enhancing Adversarial Transferability via Component-Wise Transformation—0
Differentiable Adversarial Attacks for Marked Temporal Point ProcessesCode0
Salient Information Preserving Adversarial Training Improves Clean and Robust Accuracy—0
Show:102550
← PrevPage 13 of 181Next →
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