Digital image steganalysis: A survey on paradigm shift from machine learning to deep learning based techniques

Abstract Steganography, a branch of data hiding techniques aims to hide confidential information within any digital media by obscuring the existence of hidden information. On the contrary, steganalysis aims to detect steganography. With the advent of powerful steganographic algorithms, the process of cracking them became very challenging. Traditional steganalysis following machine learning principle employs a two‐step process, with first process extracting highly sophisticated features capable of discriminating hidden message from original data and second process classifying the input as innocent or guilty. In recent years, deep learning which has its roots in artificial neural networks emerged as a brilliant alternative for many computer vision tasks. A review of recent research works in deep learning based digital image steganalysis is presented here. The paradigm shift from machine learning approaches to employing more promising deep learning architectures, observed with the current research community and hence in literature has been presented here in chronological order. Deep learning can unify the two‐step process into a single process by giving the ability for machine to learn end‐to‐end by itself. The use of Convolutional Neural Networks to perform steganalysis in spatial or transform or combination of both domains has effectively lowered the detection error rates