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 881–890 of 1808 papers

TitleStatusHype
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems—0
Biologically inspired protection of deep networks from adversarial attacks—0
Adversarial Attack Against Images Classification based on Generative Adversarial Networks—0
Improved Adversarial Training via Learned Optimizer—0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning—0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
Improving adversarial robustness of deep neural networks by using semantic information—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems—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