A Comprehensive Study of Deep Learning-based Covert Communication

Abstract

Deep learning-based methods have been popular in multimedia analysis tasks, including classification, detection, segmentation, and so on. In addition to conventional applications, this model can be widely used for cover communication, i.e., information hiding. This article presents a review of deep learning-based covert communication scheme for protecting digital contents, devices, and models. In particular, we discuss the background knowledge, current applications, and constraints of existing deep learning-based information hiding schemes, identify recent challenges, and highlight possible research directions. Further, major role of deep learning in the area of information hiding are highlighted. Then, the contribution of surveyed scheme is also summarized and compared in the context of estimation of design objectives, approaches, evaluation metric, and weaknesses. We believe that this survey can pave the way to new research in this crucial field of information hiding in deep-learning environment.