Testing Deep Neural Network Programs

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In recent years, artificial intelligence and in particular Machine Learning (ML) have been the core of research and development in most companies around the world. Techniques such as deep learning have been employed in many contexts and provided astonishing results. However, as we employ these ML techniques on a variety of applications and contexts, our everyday life becomes more dependent on their performance. For example, in the context of safety critical or mission critical applications a reliable, robust, safe, and secure ML is a must. Thus, in this project, we propose studying techniques for testing ML programs (particularly in the context of object detection in videos using deep learning) to understand how good the existing techniques are in terms of detecting real defects and how we can improve those techniques with respect to their shortcomings in our case study with OASIS Inc company.