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 151–160 of 176 papers

TitleStatusHype
Stealing Machine Learning Models via Prediction APIsCode0
Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope TheoryCode0
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsCode0
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement LearningCode0
Safe and Robust Watermark Injection with a Single OoD ImageCode0
VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional SpacesCode0
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selectionCode0
Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View DataCode0
Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual ExplanationsCode0
A Hard-Label Cryptanalytic Extraction of Non-Fully Connected Deep Neural Networks using Side-Channel AttacksCode0
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Benchmark Results

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