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
On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction—0
Better Decisions through the Right Causal World Model—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
Differentially private fine-tuned NF-Net to predict GI cancer type—0
From Counterfactuals to Trees: Competitive Analysis of Model Extraction AttacksCode0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI—0
"FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection"—0
HoneypotNet: Backdoor Attacks Against Model Extraction—0
Bounding-box Watermarking: Defense against Model Extraction Attacks on Object Detectors—0
Few-shot Model Extraction Attacks against Sequential Recommender Systems—0
A Hard-Label Cryptanalytic Extraction of Non-Fully Connected Deep Neural Networks using Side-Channel AttacksCode0
Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation Attack against EaaS WatermarkCode0
Robust and Minimally Invasive Watermarking for EaaSCode0
Efficient Model Extraction via Boundary Sampling—0
Efficient and Effective Model ExtractionCode0
CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble—0
Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Model Watermarking—0
VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional SpacesCode0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
QUEEN: Query Unlearning against Model Extraction—0
Privacy Implications of Explainable AI in Data-Driven Systems—0
Beyond Slow Signs in High-fidelity Model ExtractionCode0
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Benchmark Results

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