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 971–980 of 1808 papers

TitleStatusHype
Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning—0
Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous—0
Robust and Effective Grammatical Error Correction with Simple Cycle Self-Augmenting—0
NaturalAE: Natural and Robust Physical Adversarial Examples for Object Detectors—0
Robust Certification for Laplace Learning on Geometric Graphs—0
Robust Constrained Reinforcement Learning—0
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack—0
Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks—0
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey—0
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack—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