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 41–50 of 176 papers

TitleStatusHype
DAWN: Dynamic Adversarial Watermarking of Neural NetworksCode0
GUIDO: A Hybrid Approach to Guideline Discovery & Ordering from Natural Language TextsCode0
Robust and Minimally Invasive Watermarking for EaaSCode0
Deep Neural Network Fingerprinting by Conferrable Adversarial ExamplesCode0
An Approach for Process Model Extraction By Multi-Grained Text ClassificationCode0
Model Extraction Attacks on Graph Neural Networks: Taxonomy and RealizationCode0
Efficient and Effective Model ExtractionCode0
FLuID: Mitigating Stragglers in Federated Learning using Invariant DropoutCode0
A Hard-Label Cryptanalytic Extraction of Non-Fully Connected Deep Neural Networks using Side-Channel AttacksCode0
Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View DataCode0
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Benchmark Results

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