Privacy Testing for Deep Learning
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Updated
Jul 20, 2023 - Python
Privacy Testing for Deep Learning
A micro-service reference test application for model extraction, cloud management, energy efficiency, power prediction, single- and multi-tier auto-scaling
General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems
Analytic tableau based minimal model generator, model checker and theorem prover for first-order logic with modal extensions
Simple machine learning in Python/Tensorflow with model saving
Ecore metamodel reverse engineering: Automatically extract EMF metamodels from Java code.
📄 [Talk] OFFZONE 2022 / ODS Data Halloween 2022: Black-box attacks on ML models + with use of open-source tools
MEME: Generating RNN Model Explanations via Model Extraction
CME: Concept-based Model Extraction
A neural network model builder, leveraging a neuro-symbolic interface.
Marich is a model-agnostic extraction algorithm. It uses a public data to query a private model, aggregates the predicted labels, and construct a distributionall equivalent/max-information leaking extracted model.
The Labelled Transition Systems Extractor tool project
Model Reconstruction from Counterfactual Explanations
Extension for the LTS Extractor platform, which is used for enabling the communication with the methods for generating model analysis, and to ease the management of log files throughout the platform.
Collection of the TeX files and figures used to create my UofA CS master's thesis
Serverless Application Extraction System (SAES)
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