Steganography and steganalysis for digital image enhanced Forensic analysis and recommendations
Kristian D. Michaylov, Dipti Kapoor Sarmah
Abstract
Image steganography and steganalysis, which involve concealing and uncovering hidden data within images, have gained significant attention in recent years, finding applications in various fields like military, medicine, e-government, and social media. Despite their importance in real-world applications, some practical aspects remain unaddressed. To bridge this gap, the current study compares image steganography and steganalysis tools and techniques for Digital Forensic Investigators (DFIs) to uncover concealed information in images. We perform a thorough review of Artificial Intelligence, statistical, and signature steganalysis methods, assesses both free and paid versions, and experiments with various image features like size, colour, mean square error (MSE), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) using a JPEG/PNG dataset. The research provides valuable insights for professionals in cybersecurity. The originality of this research resides in the fact that, although previous studies have been conducted in this area, none have explicitly examined the analysis of the selected tools—F5, Steghide, Outguess for image steganography, and Aletheia, StegExpose for image steganalysis—and their application to JPEG image analysis.