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 71–80 of 176 papers

TitleStatusHype
Extraction of Complex DNN Models: Real Threat or Boogeyman?—0
Few-shot Model Extraction Attacks against Sequential Recommender Systems—0
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations—0
First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data—0
A Review of Confidentiality Threats Against Embedded Neural Network Models—0
"FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection"—0
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
Bounding-box Watermarking: Defense against Model Extraction Attacks on Object Detectors—0
A Novel Watermarking Framework for Ownership Verification of DNN Architectures—0
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Benchmark Results

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