Stego Analysis : A Comparative Analysis of Steganographic Tools

Abstract

Today, any digital file that contain sensitive information needs to be secured, and this is where steganography comes in to present unique means to embed secret messages in normal digital files like images, videos, and audio files. Open-source steganography tools are so numerous that users are challenged in selecting which tools most adequately meet their specific needs. With each medium/tool available providing enhanced cloaking depending on the input file the ambiguity for an no layman user increases substantially. As part of the StegoAnalysis project, our vision is to offer an allencompassing platform that will analyze and compare all the open-source steganography tools available and give the user a well guided way to make a choice without blindfolds in the employment of steganography and steganography tools. Key features of the StegoAnalysis project include, as mentioned above, A comprehensive analysis of the security features of all open-source steganography tools based on whose result is conducted comparative analysis of said tools to determine a hierarchy of use depending on the given factors i.e., input file type, file size, use of encryption and if performance of encryption algorithm. These factors enable the project to allow users to detect steganography for their desired files and use the system to obtain valuable recommendations for the employment of a specific steganography tool for their transfer cover files. In this report, we describe the architectural design of the system for the StegoAnalysis project, both the frontend and backend components. The frontend itself is completed in ReactJS, offering a simple, interactive and friendly interface for users to use as they explore tools. This consists of a landing page that introduces the system, a search and filtering interface to choose tools according to their level of security and tool type, a graphical tool comparison page, and an educational resources page detailing steganography concepts and best practices. HTTPS secure data transfer is also secured with design, and finally, it is responsive for perfect viewing on various devices. Built with Python and Flask, the backend supplies the needed APIs to search for filtering tools based on user input, get details about a certain tool, as well as access educational resources. In addition, a user management API is provided optionally for login and submission capabilities. This is also used to integrate machine learning models for tool recommendation based on your user input and also a model to analyze tool performance. The data model allows one collection to store steganography tool data and an educational collection, plus an optional user information collection. This structure guarantees that this system can easily handle and present information to users. In fact, the StegoAnalysis project is an overall effort to simplify the process of steganography tool selection by offering clear evaluations, user friendly comparisons and useful recommendations. The project enables stakeholders to employ steganography via available steganography tools best matched to their organizational and individual needs based on the comprehensively studied factors.