Safe and Secure Autonomous Systems

Dear Colleagues,

The rapid progression of deep learning in the past decade has led to the development of learning-enabled systems, such as robotics systems, medical diagnosis systems, and Industry 5.0. However, rigorous methods to enforce the safety and security of these systems are still needed. This article collection aims to bring together research on how to identify, rectify, and verify the safety and security risks of learning-enabled systems. We welcome various forms of contributions, such as original research, survey papers, case studies, and tool demonstrations. The scope of this Topic includes, but is not limited to, the following:

1. Attack and defense mechanisms in the machine learning model;
2. Formal verification;
3. Practical testing methods;
4. Privacy-preserving machine learning;
5. Safety assurance of autonomous systems;
6. Fairness and ethics of autonomous systems;
7. Explainable AI for autonomous systems;
8. Reliability assessment.

Prof. Dr. Xiaowei Huang
Dr. Wenjie Ruan
Dr. Xingyu Zhao
Topic Editors