A Practical Overview of Safety Concerns and Mitigation Methods for Visual Deep Learning Algorithms

Abstract

This paper proposes a practical list of safety concerns and mitigation methods for visual deep learning algorithms. The growing success of deep learning algorithms in solving non-linear and complex problems has recently attracted the attention of safety-critical applications. While the state-of-the-art methods achieve high performance in synthetic and real-case scenarios, it is impossible to verify/validate their reliability based on currently available safety standards. Recent works try to solve the issue by providing a list of safety concerns and mitigation methods in generic machine learning algorithms from the standards’ perspective. However, these solutions are either vague, and non-practical when dealing with deep learning methods in real-case scenarios, or they are shallow and fail to address all potential safety concerns. This paper provides an in-depth look at the underlying cause of faults in a visual deep learning algorithm to find a practical and complete safety concern list with potential state-of-the-art mitigation strategies.