  Steganography and steganalysis are two interrelated aspects of the field of
information security. Steganography seeks to conceal communications, whereas
steganalysis is aimed to either find them or even, if possible, recover the
data they contain. Steganography and steganalysis have attracted a great deal
of interest, particularly from law enforcement. Steganography is often used by
cybercriminals and even terrorists to avoid being captured while in possession
of incriminating evidence, even encrypted, since cryptography is prohibited or
restricted in many countries. Therefore, knowledge of cutting-edge techniques
to uncover concealed information is crucial in exposing illegal acts. Over the
last few years, a number of strong and reliable steganography and steganalysis
techniques have been introduced in the literature. This review paper provides a
comprehensive overview of deep learning-based steganalysis techniques used to
detect hidden information within digital media. The paper covers all types of
cover in steganalysis, including image, audio, and video, and discusses the
most commonly used deep learning techniques. In addition, the paper explores
the use of more advanced deep learning techniques, such as deep transfer
learning (DTL) and deep reinforcement learning (DRL), to enhance the
performance of steganalysis systems. The paper provides a systematic review of
recent research in the field, including data sets and evaluation metrics used
in recent studies. It also presents a detailed analysis of DTL-based
steganalysis approaches and their performance on different data sets. The
review concludes with a discussion on the current state of deep learning-based
steganalysis, challenges, and future research directions.
