Shipped: Neural Network Model Reverse Engineering Toolkit

An AI model analysis proof of concept that examines trained machine learning models to reveal architectural details, parameter counts, computational complexity, estimated memory requirements, and deployment characteristics.

Supports models from frameworks including TensorFlow/Keras, PyTorch, ONNX, and scikit-learn. The project also explores preliminary AI security considerations such as model transparency, potential attack exposure, and deployment risks.

Designed for AI developers, researchers, and security practitioners interested in understanding model internals and assessing model characteristics before deployment.