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 561–570 of 1808 papers

TitleStatusHype
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical AnalysisCode0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks—0
Transferable 3D Adversarial Shape Completion using Diffusion ModelsCode0
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images—0
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition SystemsCode0
DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques—0
Rethinking Targeted Adversarial Attacks For Neural Machine TranslationCode0
Self-Supervised Representation Learning for Adversarial Attack Detection—0
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer TrackersCode0
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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