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 151–160 of 176 papers

TitleStatusHype
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
Differentially private fine-tuned NF-Net to predict GI cancer type—0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data—0
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations—0
DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking—0
Efficiently Learning Any One Hidden Layer ReLU Network From Queries—0
Efficiently Learning One Hidden Layer ReLU Networks From Queries—0
Efficient Model Extraction via Boundary Sampling—0
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Benchmark Results

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