  Over the past decades, deep learning (DL) systems have achieved tremendous
success and gained great popularity in various applications, such as
intelligent machines, image processing, speech processing, and medical
diagnostics. Deep neural networks are the key driving force behind its recent
success, but still seem to be a magic black box lacking interpretability and
understanding. This brings up many open safety and security issues with
enormous and urgent demands on rigorous methodologies and engineering practice
for quality enhancement. A plethora of studies have shown that the
state-of-the-art DL systems suffer from defects and vulnerabilities that can
lead to severe loss and tragedies, especially when applied to real-world
safety-critical applications. In this paper, we perform a large-scale study and
construct a paper repository of 223 relevant works to the quality assurance,
security, and interpretation of deep learning. We, from a software quality
assurance perspective, pinpoint challenges and future opportunities towards
universal secure deep learning engineering. We hope this work and the
accompanied paper repository can pave the path for the software engineering
community towards addressing the pressing industrial demand of secure
intelligent applications.
