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%

Performance Analysis on Text Steganalysis Method Using A Computational Intelligence Approach

In this paper, a critical view of the utilization ofcomputational intelligence approach from the text steganalysisperspective is presented. This paper proposes a formalization ofgenetic algorithm method in order to detect hidden message on ananalyzed text. Five metric parameters such as running time, fitnessvalue, average mean probability, variance probability, and standarddeviation probability were used to measure the detection performancebetween statistical methods and genetic algorithm methods.Experiments conducted using both methods showed that geneticalgorithm method performs much better than statistical method,especially in detecting short analyzed texts. Thus, the findings showedthat the genetic algorithm method on analyzed stego text is verypromising. For future work, several significant factors such as datasetenvironment, searching process and types of fitness values throughother intelligent methods of computational intelligence should beinvestigated

Detection of Steganography-Producing Software Artifacts on Crime-Related Seized Computers

Steganography is the art and science of hiding information within information so that an observer does not know that communication is taking place. Bad actors passing information using steganography are of concern to the national security establishment and law enforcement. An attempt was made to determine if steganography was being used by criminals to communicate information. Web crawling technology was used and images were downloaded from Web sites that were considered as likely candidates for containing information hidden using steganographic techniques. A detection tool was used to analyze these images. The research failed to demonstrate that steganography was prevalent on the public Internet. The probable reasons included the growth and availability of large number of steganography-producing tools and the limited capacity of the detection tools to cope with them. Thus, a redirection was introduced in the methodology and the detection focus was shifted from the analysis of the ‘product’ of the steganography-producing software; viz. the images, to the \u27artifacts’ left by the steganography-producing software while it is being used to generate steganographic images. This approach was based on the concept of ‘Stego-Usage Timeline’. As a proof of concept, a sample set of criminal computers was scanned for the remnants of steganography-producing software. The results demonstrated that the problem of ‘the detection of the usage of steganography’ could be addressed by the approach adopted after the research redirection and that certain steganographic software was popular among the criminals. Thus, the contribution of the research was in demonstrating that the limitations of the tools based on the signature detection of steganographically altered images can be overcome by focusing the detection effort on detecting the artifacts of the steganography-producing tools. Keywords: steganography, signature detection, file artifact detection

Digital steganalysis: Computational intelligence approach

In this paper, we present a consolidated view of digital media steganalysis from the perspective of computational intelligence.In our analysis the digital media steganalysis is divided into three domains which are image steganalysis, audio steganalysis, and video steganalysis.Three major computational intelligence methods have also been identified in the steganalysis domains which are bayesian, neural network, and genetic algorithm.Each of these methods has its own pros and cons

An Overview of Steganography for the Computer Forensics Examiner (Updated Version, February 2015)

Steganography is the art of covered or hidden writing. The purpose of steganography is covert communication-to hide the existence of a message from a third party. This paper is intended as a high-level technical introduction to steganography for those unfamiliar with the field. It is directed at forensic computer examiners who need a practical understanding of steganography without delving into the mathematics, although references are provided to some of the ongoing research for the person who needs or wants additional detail. Although this paper provides a historical context for steganography, the emphasis is on digital applications, focusing on hiding information in online image or audio files. Examples of software tools that employ steganography to hide data inside of other files as well as software to detect such hidden files will also be presented. An edited version originally published in the July 2004 issues of Forensic Science Communications

Digital Steganalysis: Review on Recent Approaches

Abstract:Steganography is the art and science of secret communication, aiming to conceal the existence of a communication, which has been used in military, and perhaps terrorists. Steganography in the modern day sense of the word usually refers to information or a file that has been concealed inside a digital Picture, Video or Audio file. In steganography, the actual information is not maintained in its original format and thereby it is converted into an alternative equivalent multimedia file like image, video or audio, which in turn is being hidden within another object. Information Security is becoming an inseparable part of Data Communication. In order to address this Information Security, Steganography plays an important role. The digital media steganalysis is divided into three domains, which are image steganalysis, audio steganalysis, and video steganalysis. DNA sequences possess some interesting properties, which can be utilized to hide data. This paper is a review of the recent steganography techniques and utilization of DNA sequence appeared in the literature