Title: Detecting Potential Local Adversarial Examples for Human-Interpretable Defense,
Abstract: Machine learning models are increasingly used in the industry to make
decisions such as credit insurance approval. Some people may be tempted to
manipulate specific variables, such as the age or the salary, in order to get
better chances of approval. In this ongoing work, we propose to discuss, with a
first proposition, the issue of detecting a potential local adversarial example
on classical tabular data by providing to a human expert the locally critical
features for the classifier's decision, in order to control the provided
information and avoid a fraud.