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 21–30 of 176 papers

TitleStatusHype
Navigating the Deep: Signature Extraction on Deep Neural Networks—0
Explore the vulnerability of black-box models via diffusion models—0
GradEscape: A Gradient-Based Evader Against AI-Generated Text Detectors—0
MISLEADER: Defending against Model Extraction with Ensembles of Distilled ModelsCode0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning—0
On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction—0
Better Decisions through the Right Causal World Model—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
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Benchmark Results

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