  Artificial Intelligence (AI) relies heavily on deep learning - a technology
that is becoming increasingly popular in real-life applications of AI, even in
the safety-critical and high-risk domains. However, it is recently discovered
that deep learning can be manipulated by embedding Trojans inside it.
Unfortunately, pragmatic solutions to circumvent the computational requirements
of deep learning, e.g. outsourcing model training or data annotation to third
parties, further add to model susceptibility to the Trojan attacks. Due to the
key importance of the topic in deep learning, recent literature has seen many
contributions in this direction. We conduct a comprehensive review of the
techniques that devise Trojan attacks for deep learning and explore their
defenses. Our informative survey systematically organizes the recent literature
and discusses the key concepts of the methods while assuming minimal knowledge
of the domain on the readers part. It provides a comprehensible gateway to the
broader community to understand the recent developments in Neural Trojans.
