Attacks and Defenses in Explainable Artificial Intelligence

Bridge the critical gap between AI transparency and security with this essential guide to the systematic defense frameworks and ethical strategies needed to protect explainable AI (XAI) systems from sophisticated adversarial attacks.

In the artificial intelligence era, explainable AI (XAI) is an essential breakthrough that plays a vital role in unfolding complex AI model decisions and predictions. However, adversarial attacks can break XAI systems and create dangerous cyber threats. This book is a fundamental guide to the systematic framework and solutions surrounding XAI and its vulnerabilities. It presents strategies for detecting adversarial attacks and focuses on various attack scenarios and defense mechanisms essential in stimulating AI systems. The book will provide a systematic and detailed exploration of the complexity of adversarial attacks on XAI systems and propose theoretical concepts, methodological solutions, and essential tools for protecting the XAI systems against adversarial attacks. Thus, the presented book will provide insights for researchers, academicians, governments, industries, and stakeholders to fill the gap in understating the XAI theory and its real-time applications with possible solutions. It will also provide insights into the ethical considerations concerning XAI in inviting users to study and deliver moral behaviours. Lastly, it will represent the broader perspectives on XAI with its growth, applications, vulnerabilities, defence mechanisms, and ethical considerations. Moreover, the case studies are on real-life applications such as healthcare, environmental studies, finance sectors, legal systems, cybersecurity, educational studies, crewless vehicles, and industrial processes.

Amol Dattatraya Vibhute, PhD, is an Assistant Professor at the School of Cyber Security and Digital Forensics, National Forensic Sciences University, Nagpur, Maharashtra, India with more than nine years of academic experience in research and innovation. He has one international and six Indian patents under review, and one granted Indian patent to his credit and has authored and co-authored more than 65 referred journals, book chapters, and conference papers in reputed international journals and conferences. His research interests include geospatial technology, digital image processing, pattern recognition, big data analysis, the Internet of Things (IoT), and machine learning.

Rajesh Kumar Dhanaraj, PhD, is a Professor at Symbiosis International University. He has authored and edited more than 50 books, contributed more than 100 articles to national and international journals and conferences, and holds 21 patents. His research interests encompass machine learning, cyber-physical systems, and wireless sensor networks.

Malathy Sathyamoorthy, PhD, is an Assistant Professor in the Department of Information Technology, at the KPR institute of Engineering and Technology. She has published more than 25 research papers in various international journals, 22 papers in international conferences, two patents, one book, and four book chapters. Wireless sensor networks, networking, security, and machine learning are her research interests.

Paramasivam A., PhD, is an Associate Professor in the Department of Biomedical Engineering at Vel Tech Rangarajan Dr. Sagunthala Research and Development at the Institute of Science and Technology, Chennai. He has published several research papers in peer-reviewed journals and conferences. His areas of interest include the Internet of Medical Things (IoMT), edge computing, biosignal and image analysis, and artificial intelligence.