In recent years, machine learning has set higher expectations from intelligence technology. Deep learning models also have several benefits such as fast computation of complex problems, maximum application of unstructured data, reduced costs, and more. The applications based on deep learning models are used in day-to-day routines and they work on huge amount of data to achieve higher accuracy and if these models lead to inaccuracies because of malicious activities, then it would become cumbersome and thus to protect the data from the security breaches is a major concern.
This book focuses on the recent advances and challenges related to the concerns of security and privacy issues in deep learning with an emphasis on the current state-of-the-art methods, implementations, attacks, and their countermeasures. The book also discusses the challenges that need to be addressed for implementing deep learning-based security mechanisms that should have the capability of collecting or distributing data across several applications.