277 research outputs found
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 toolsâF5, Steghide, Outguess for image steganography, and Aletheia, StegExpose for image steganalysisâand their application to JPEG image analysis
Application of Steganography for Anonymity through the Internet
In this paper, a novel steganographic scheme based on chaotic iterations is
proposed. This research work takes place into the information hiding security
framework. The applications for anonymity and privacy through the Internet are
regarded too. To guarantee such an anonymity, it should be possible to set up a
secret communication channel into a web page, being both secure and robust. To
achieve this goal, we propose an information hiding scheme being stego-secure,
which is the highest level of security in a well defined and studied category
of attacks called "watermark-only attack". This category of attacks is the best
context to study steganography-based anonymity through the Internet. The
steganalysis of our steganographic process is also studied in order to show it
security in a real test framework.Comment: 14 page
Steganography: a class of secure and robust algorithms
This research work presents a new class of non-blind information hiding
algorithms that are stego-secure and robust. They are based on some finite
domains iterations having the Devaney's topological chaos property. Thanks to a
complete formalization of the approach we prove security against watermark-only
attacks of a large class of steganographic algorithms. Finally a complete study
of robustness is given in frequency DWT and DCT domains.Comment: Published in The Computer Journal special issue about steganograph
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
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