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 26–50 of 176 papers

TitleStatusHype
Automating Agential Reasoning: Proof-Calculi and Syntactic Decidability for STIT Logics—0
Efficient Model Extraction via Boundary Sampling—0
Beyond Labeling Oracles: What does it mean to steal ML models?—0
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs—0
An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM—0
Emerging AI Security Threats for Autonomous Cars -- Case Studies—0
Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security—0
A Survey on Event-based News Narrative Extraction—0
A Framework for Understanding Model Extraction Attack and Defense—0
DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking—0
A framework for the extraction of Deep Neural Networks by leveraging public data—0
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations—0
Efficiently Learning Any One Hidden Layer ReLU Network From Queries—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
A Review of Confidentiality Threats Against Embedded Neural Network Models—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
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—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
CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble—0
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Benchmark Results

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