Aletheia: an open-source toolbox for steganalysis

Abstract

Steganalysis is the practice of detecting the presence of hidden information within digital media, such as images, audio, or video. It involves analyzing the media for signs of steganography, which is a set of techniques used to conceal information within the carrier file. Steganalysis techniques can include statistical analysis, visual inspection, and machine learning algorithms to uncover hidden data. The goal of steganalysis is to determine whether a file contains covert information and potentially identify the steganographic method used. Steganalysis has become increasingly important in the face of rising spying and stegomalware threats, particularly in the context of data exfiltration. In this scenario, malicious actors leverage steganographic techniques to conceal sensitive data within innocent-looking files, evading traditional security measures. By detecting and analyzing such covert communication channels, steganalysis helps to identify and prevent data exfiltration attempts, safeguarding critical information and preventing it from falling into the wrong hands. In recent years, there has been a significant growth in the interest of researchers towards the field of steganalysis. The application of deep learning (Boroumand et al., 2019; Yousfi et al., 2020) in steganalysis has opened up new avenues for research, leading to improved detection rates and enhanced accuracy. As the field continues to evolve, experts are actively exploring novel architectures and training methodologies to further refine the performance of deep learning-based steganalysis.