MINT-Demo: Membership Inference Test Demonstrator
This addresses the need for transparency in AI training processes, particularly for stakeholders concerned with data privacy and model accountability, though it is incremental as it builds on existing membership inference techniques.
The paper tackles the problem of determining whether specific data was used in training machine learning models, achieving up to 89% accuracy in experiments with face recognition models and public databases containing over 22M images.
We present the Membership Inference Test Demonstrator, to emphasize the need for more transparent machine learning training processes. MINT is a technique for experimentally determining whether certain data has been used during the training of machine learning models. We conduct experiments with popular face recognition models and 5 public databases containing over 22M images. Promising results, up to 89% accuracy are achieved, suggesting that it is possible to recognize if an AI model has been trained with specific data. Finally, we present a MINT platform as demonstrator of this technology aimed to promote transparency in AI training.