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 51–60 of 176 papers

TitleStatusHype
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
Watermarking Counterfactual ExplanationsCode0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks—0
Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope TheoryCode0
Learnable Linguistic Watermarks for Tracing Model Extraction Attacks on Large Language Models—0
Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual ExplanationsCode0
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines—0
Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View DataCode0
Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices—0
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Benchmark Results

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