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
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
Differentially private fine-tuned NF-Net to predict GI cancer type—0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data—0
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations—0
DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking—0
Efficiently Learning Any One Hidden Layer ReLU Network From Queries—0
Efficiently Learning One Hidden Layer ReLU Networks From Queries—0
Efficient Model Extraction via Boundary Sampling—0
Emerging AI Security Threats for Autonomous Cars -- Case Studies—0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security—0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning—0
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System—0
Explore the vulnerability of black-box models via diffusion models—0
Exploring Connections Between Active Learning and Model Extraction—0
EXPLORING VULNERABILITIES OF BERT-BASED APIS—0
Extraction of Complex DNN Models: Real Threat or Boogeyman?—0
EZClone: Improving DNN Model Extraction Attack via Shape Distillation from GPU Execution Profiles—0
Fault Injection and Safe-Error Attack for Extraction of Embedded Neural Network Models—0
FDINet: Protecting against DNN Model Extraction via Feature Distortion Index—0
Few-shot Model Extraction Attacks against Sequential Recommender Systems—0
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations—0
First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data—0
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Benchmark Results

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