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

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Showing 31–40 of 176 papers

TitleStatusHype
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced DistillationCode1
VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional SpacesCode0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
QUEEN: Query Unlearning against Model Extraction—0
Privacy Implications of Explainable AI in Data-Driven Systems—0
Beyond Slow Signs in High-fidelity Model ExtractionCode0
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
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Benchmark Results

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