SOTAVerified

Model extraction

Model extraction attacks, aka model stealing attacks, are used to extract the parameters from the target model. Ideally, the adversary will be able to steal and replicate a model that will have a very similar performance to the target model.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 141–150 of 176 papers

TitleStatusHype
A Knowledge Representation Approach to Automated Mathematical Modelling—0
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack—0
Leveraging Extracted Model Adversaries for Improved Black Box Attacks—0
Now You See Me (CME): Concept-based Model ExtractionCode1
Model Extraction Attacks on Graph Neural Networks: Taxonomy and RealizationCode0
MEME: Generating RNN Model Explanations via Model ExtractionCode1
Model extraction from counterfactual explanationsCode0
Stealing Deep Reinforcement Learning Models for Fun and Profit—0
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction LibraryCode1
Cryptanalytic Extraction of Neural Network ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1three-step-originalExact Match0.17—Unverified