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 111–120 of 176 papers

TitleStatusHype
Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack—0
Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC—0
Towards dialogue based, computer aided software requirements elicitation—0
Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation—0
Towards Security Threats of Deep Learning Systems: A Survey—0
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them—0
Using Python for Model Inference in Deep Learning—0
Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks—0
Watermarking Graph Neural Networks based on Backdoor Attacks—0
Sparsity-driven Digital Terrain Model Extraction—0
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Benchmark Results

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