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 121–130 of 176 papers

TitleStatusHype
High Accuracy and High Fidelity Extraction of Neural Networks—0
HODA: Protecting DNNs Against Model Extraction Attacks via Hardness of Samples—0
HoneypotNet: Backdoor Attacks Against Model Extraction—0
Increasing the Cost of Model Extraction with Calibrated Proof of Work—0
Interpretability via Model Extraction—0
Interpreting Blackbox Models via Model Extraction—0
Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs—0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
Learnable Linguistic Watermarks for Tracing Model Extraction Attacks on Large Language Models—0
Leveraging Extracted Model Adversaries for Improved Black Box Attacks—0
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Benchmark Results

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