A New Blind Method for Detecting Novel Steganography

Steganography is the art of hiding a message in plain sight. Modern steganographic tools that conceal data in innocuous-looking digital image files are widely available. The use of such tools by terrorists, hostile states, criminal organizations, etc., to camouflage the planning and coordination of their illicit activities poses a serious challenge. Most steganography detection tools rely on signatures that describe particular steganography programs. Signature-based classifiers offer strong detection capabilities against known threats, but they suffer from an inability to detect previously unseen forms of steganography. Novel steganography detection requires an anomaly-based classifier. This paper describes and demonstrates a blind classification algorithm that uses hyper-dimensional geometric methods to model steganography-free jpeg images. The geometric model, comprising one or more convex polytopes, hyper-spheres, or hyper-ellipsoids in the attribute space, provides superior anomaly detection compared to previous research. Experimental results show that the classifier detects, on average, 85.4% of Jsteg steganography images with a mean embedding rate of 0.14 bits per pixel, compared to previous research that achieved a mean detection rate of just 65%. Further, the classification algorithm creates models for as many training classes of data as are available, resulting in a hybrid anomaly/signature or signature-only based classifier, which increases Jsteg detection accuracy to 95%

Universal Image Steganalytic Method

In the paper we introduce a new universal steganalytic method in JPEG file format that is detecting well-known and also newly developed steganographic methods. The steganalytic model is trained by MHF-DZ steganographic algorithm previously designed by the same authors. The calibration technique with the Feature Based Steganalysis (FBS) was employed in order to identify statistical changes caused by embedding a secret data into original image. The steganalyzer concept utilizes Support Vector Machine (SVM) classification for training a model that is later used by the same steganalyzer in order to identify between a clean (cover) and steganographic image. The aim of the paper was to analyze the variety in accuracy of detection results (ACR) while detecting testing steganographic algorithms as F5, Outguess, Model Based Steganography without deblocking, JP Hide&Seek which represent the generally used steganographic tools. The comparison of four feature vectors with different lengths FBS (22), FBS (66) FBS(274) and FBS(285) shows promising results of proposed universal steganalytic method comparing to binary methods

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