Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions

• URL: http://arxiv.org/abs/2308.04522v3
• Date: Tue, 12 Mar 2024 00:16:38 GMT
• Title: Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions
• Authors: Hamza Kheddar, Mustapha Hemis, Yassine Himeur, David Meg\'ias, Abbes Amira
• Abstract summary: Steganography and steganalysis have attracted a great deal of interest, particularly from law enforcement. This review paper provides a comprehensive overview of deep learning-based steganalysis techniques.
• Score: 3.2007743266566617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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