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 81–90 of 176 papers

TitleStatusHype
Model Extraction and Defenses on Generative Adversarial Networks—0
Model Extraction Attack against Self-supervised Speech Models—0
Model Extraction Attacks Against Reinforcement Learning Based Controllers—0
Model Extraction Attacks against Recurrent Neural Networks—0
Model Extraction Attacks on Split Federated Learning—0
Model Extraction Attacks Revisited—0
Model Extraction Warning in MLaaS Paradigm—0
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack—0
NASPY: Automated Extraction of Automated Machine Learning Models—0
NaturalFinger: Generating Natural Fingerprint with Generative Adversarial Networks—0
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Benchmark Results

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