Feed-forward, fully-connected Neural Networks (via Tensorflow/Keras) and Decision trees on the ML side
The IBM Cplex Optimizer and the Goole Or-Tools Mixed Integer Linear solver wrapper on the optimization side.
Execution environment: interpreted
Version of the environment: Python 3.X
The EMLlib can be used as an exact approach for the verification of ML systems and for the generation of adversarial examples, with the corresponding advantages (completeness) and disadvantages (limited scalability). As such, the library could be used by malicious actor to fool an existing ML system (with known structure and weights), or by a lawful actor to certificate or improve the robustness of an ML system.
The tool is designed simply for model integration so that GDRP restrictions do not apply to the library itself. The models (ML & optimization) being integrated should however respect GDPR individually.