In this platform, we introduce a method called Membership Inference Test (MINT). MINT introduces a novel approach designed to empirically assess whether specific data was utilized during the training of Artificial Intelligence (AI) models. It serves as an auditing tool where we train the so-called MINT Models, which are binary classifiers detecting which data was employed in the training of a model. The platform's objective is to audit the maximum number of models possible, allowing anyone to verify whether a particular image was used in training. The platform will be periodically updated with new audited models along with their corresponding MINT Models. For technical details, refer to our Full Paper: [Click]. In the accompanying zip file, we provide 10 images that were used and not used in the training of the models.