Aletheia: an open-source toolbox for steganalysis Daniel Lerch-Hostalot 1,2,3 and David Megías 1,2,3 1 Internet Interdisciplinary Institute (IN3), Barcelona, Spain 2 Universitat Oberta de Catalunya (UOC), Barcelona, Spain 3 CYBERCAT-Center for Cybersecurity Research of Catalonia, Barcelona, Spain DOI: 10.21105/joss.05982 Software • Review • Repository • Archive Editor: Marcel Stimberg Reviewers: • @YassineYousfi • @ragibson Submitted: 12 July 2023 Published: 16 January 2024 License Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Summary 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. Statement of need Aletheia addresses two main needs. Firstly, it aims to provide specialized analysts with a tool that implements modern steganalysis algorithms, leveraging deep learning techniques. These algorithms are designed to effectively handle even the most advanced steganography techniques. Secondly, Aletheia serves as a valuable tool for researchers by simplifying the process of conducting experiments and comparing methods. It includes simulators for common algorithms (Hetzl & Mutzel, 2005; Provos, 2001; Sharp, 2001) as well as state-of-the-art steganography methods (Fridrich et al., 2007; Guo et al., 2014; Holub et al., 2014; Li et al., 2014; Zhang et al., 2019), enabling researchers to prepare and evaluate their work efficiently. On the other hand, to the best of the authors’ knowledge, Aletheia stands out as the sole steganalysis tool currently available that incorporates the latest detection techniques (Lerch- Hostalot & Megı́as, 2019; Megı́as & Lerch-Hostalot, 2023) specifically designed to address the challenges posed by Cover Source Mismatch (CSM) in real-world steganalysis scenarios (Ker et al., 2013). This capability is particularly significant for conducting effective steganalysis in practical applications. Lerch-Hostalot, & Megías. (2024). Aletheia: an open-source toolbox for steganalysis. Journal of Open Source Software, 9 (93), 5982. https: //doi.org/10.21105/joss.05982. 1