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 51–100 of 176 papers

TitleStatusHype
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
On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction—0
GradEscape: A Gradient-Based Evader Against AI-Generated Text Detectors—0
A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks—0
Adversarial Exploitation of Policy Imitation—0
Adversarial Model Extraction on Graph Neural Networks—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
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
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
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
Quantifying (Hyper) Parameter Leakage in Machine Learning—0
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines—0
QUEEN: Query Unlearning against Model Extraction—0
Revealing Secrets From Pre-trained Models—0
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack—0
Security and Privacy Challenges in Deep Learning Models—0
Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models—0
SEEK: model extraction attack against hybrid secure inference protocols—0
Sparsity-driven Digital Terrain Model Extraction—0
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption—0
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Benchmark Results

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