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 1241–1250 of 1808 papers

TitleStatusHype
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
An Adversarial Attack via Feature Contributive Regions—0
Practical Order Attack in Deep Ranking—0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem—0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
Patch-wise++ Perturbation for Adversarial Targeted AttacksCode1
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
Sparse Adversarial Attack to Object DetectionCode1
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition—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