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

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Showing 1331–1340 of 1808 papers

TitleStatusHype
Robust Text CAPTCHAs Using Adversarial Examples—0
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning—0
Towards Robustness of Deep Neural Networks via Regularization—0
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces—0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval—0
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks—0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem—0
Stabilized Medical Attacks—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
Practical Order Attack in Deep Ranking—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