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 76–100 of 176 papers

TitleStatusHype
MEAOD: Model Extraction Attack against Object Detectors—0
MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI—0
Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Accelerator—0
Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism—0
Model Extraction and Adversarial Attacks on Neural Networks using Switching Power Information—0
Model Extraction and Defenses on Generative Adversarial Networks—0
Model Extraction Attack against Self-supervised Speech Models—0
Model Extraction Attacks Against Reinforcement Learning Based Controllers—0
Model Extraction Attacks against Recurrent Neural Networks—0
Model Extraction Attacks on Split Federated Learning—0
Model Extraction Attacks Revisited—0
Model Extraction Warning in MLaaS Paradigm—0
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack—0
NASPY: Automated Extraction of Automated Machine Learning Models—0
NaturalFinger: Generating Natural Fingerprint with Generative Adversarial Networks—0
Navigating the Deep: Signature Extraction on Deep Neural Networks—0
On the amplification of security and privacy risks by post-hoc explanations in machine learning models—0
On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction—0
Ownership Protection of Generative Adversarial Networks—0
Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems—0
Power-Based Attacks on Spatial DNN Accelerators—0
Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices—0
Privacy Implications of Explainable AI in Data-Driven Systems—0
ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction—0
Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Model Watermarking—0
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Benchmark Results

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