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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 176 papers

TitleStatusHype
Entangled Watermarks as a Defense against Model ExtractionCode1
ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public DataCode0
Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism—0
Model Extraction Attacks against Recurrent Neural Networks—0
Adversarial Model Extraction on Graph Neural Networks—0
Deep Neural Network Fingerprinting by Conferrable Adversarial ExamplesCode0
Towards Security Threats of Deep Learning Systems: A Survey—0
Quantifying (Hyper) Parameter Leakage in Machine Learning—0
MaskedNet: The First Hardware Inference Engine Aiming Power Side-Channel Protection—0
Thieves on Sesame Street! Model Extraction of BERT-based APIsCode0
Extraction of Complex DNN Models: Real Threat or Boogeyman?—0
High Accuracy and High Fidelity Extraction of Neural Networks—0
Automating Agential Reasoning: Proof-Calculi and Syntactic Decidability for STIT Logics—0
Adversarial Exploitation of Policy Imitation—0
DAWN: Dynamic Adversarial Watermarking of Neural NetworksCode0
A framework for the extraction of Deep Neural Networks by leveraging public data—0
An Approach for Process Model Extraction By Multi-Grained Text ClassificationCode0
Exploring Connections Between Active Learning and Model Extraction—0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data—0
Model Extraction Warning in MLaaS Paradigm—0
Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC—0
Interpretability via Model Extraction—0
Interpreting Blackbox Models via Model Extraction—0
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking—0
Stealing Machine Learning Models via Prediction APIsCode0
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Benchmark Results

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