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
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
Few-shot Model Extraction Attacks against Sequential Recommender Systems—0
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations—0
First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data—0
"FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection"—0
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models—0
Grey-box Extraction of Natural Language Models—0
GrOVe: Ownership Verification of Graph Neural Networks using Embeddings—0
HODA: Hardness-Oriented Detection of Model Extraction Attacks—0
High Accuracy and High Fidelity Extraction of Neural Networks—0
HODA: Protecting DNNs Against Model Extraction Attacks via Hardness of Samples—0
HoneypotNet: Backdoor Attacks Against Model Extraction—0
Increasing the Cost of Model Extraction with Calibrated Proof of Work—0
Interpretability via Model Extraction—0
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Benchmark Results

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