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 101–125 of 176 papers

TitleStatusHype
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
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption—0
Stealing Deep Reinforcement Learning Models for Fun and Profit—0
Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack—0
Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC—0
Towards dialogue based, computer aided software requirements elicitation—0
Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation—0
Towards Security Threats of Deep Learning Systems: A Survey—0
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them—0
Using Python for Model Inference in Deep Learning—0
Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks—0
Watermarking Graph Neural Networks based on Backdoor Attacks—0
Sparsity-driven Digital Terrain 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
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Benchmark Results

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