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 1061–1070 of 1808 papers

TitleStatusHype
State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning—0
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems—0
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning—0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples—0
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling—0
Stochastic Variance Reduced Ensemble Adversarial Attack—0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents—0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models—0
Superclass Adversarial Attack—0
SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation—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