Exploiting explanations for model extraction via knowledge distillation and mitigation with private counterfactuals
Omran Ayoub
In recent years, there has been a notable increase in the deployment of machine learning (ML) models as services (MLaaS) across diverse production software applications. In parallel, explainable AI (XAI) continues to evolve, addressing the necessity for transparency in ML models. XAI techniques aim to enhance the transparency of ML models by providing insights, in terms of model's explanations, into their decision-making process. At the same time, some MLaaS platforms now offer explanations alon