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 126–150 of 176 papers

TitleStatusHype
A framework for the extraction of Deep Neural Networks by leveraging public data—0
A Framework for Understanding Model Extraction Attack and Defense—0
A Knowledge Representation Approach to Automated Mathematical Modelling—0
An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM—0
An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network—0
A Novel Watermarking Framework for Ownership Verification of DNN Architectures—0
A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters—0
A Review of Confidentiality Threats Against Embedded Neural Network Models—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
A Survey on Event-based News Narrative Extraction—0
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models—0
Automated Data-Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function—0
Automating Agential Reasoning: Proof-Calculi and Syntactic Decidability for STIT Logics—0
Better Decisions through the Right Causal World Model—0
Beyond Labeling Oracles: What does it mean to steal ML models?—0
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs—0
BODAME: Bilevel Optimization for Defense Against Model Extraction—0
Bounding-box Watermarking: Defense against Model Extraction Attacks on Object Detectors—0
Bound Your Models! How to Make OWL an ASP Modeling Language—0
Business Process Text Sketch Automation Generation Using Large Language Model—0
CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
Data-Free Model Extraction Attacks in the Context of Object Detection—0
Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI—0
DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks—0
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Benchmark Results

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