Steganographer Identification

Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users called actors are guilty of using steganography, which is typically defined as the Steganographer Identification Problem (SIP). One might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection, the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when non-adaptive steganographic embedding was applied. In this chapter, we will present foundational concepts and review advanced methodologies in SIP. This chapter is self-contained and intended as a tutorial introducing the SIP in the context of media steganography.Comment: A tutorial with 30 page

Steganography and steganalysis for digital image enhanced Forensic analysis and recommendations

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