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 31–40 of 176 papers

TitleStatusHype
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
A Survey on Event-based News Narrative Extraction—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
A Framework for Understanding Model Extraction Attack and Defense—0
A framework for the extraction of Deep Neural Networks by leveraging public data—0
Data-Free Model Extraction Attacks in the Context of Object Detection—0
Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
A Review of Confidentiality Threats Against Embedded Neural Network Models—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
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Benchmark Results

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