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

Suitability of lacunarity measure for blind steganalysis

Blind steganalysis performance is influenced by several factors including the features used for classification. This paper investigates the suitability of using lacunarity measure as a potential feature vectorfor blind steganalysis. Differential Box Counting (DBC) based lacunarity measure has been employed using the traditional sequential grid (SG) and a new radial strip (RS) approach. The performance of the multi-class SVM based classifier was unfortunately not what was expected. However, the findings show that both the SG and RS lacunarity produce enough discriminating features that warrant further research

Review of steganalysis of digital images

Steganography is the science and art of embedding hidden messages into cover multimedia such as text, image, audio and video. Steganalysis is the counterpart of steganography, which wants to identify if there is data hidden inside a digital medium. In this study, some specific steganographic schemes such as HUGO and LSB are studied and the steganalytic schemes developed to steganalyze the hidden message are studied. Furthermore, some new approaches such as deep learning and game theory, which have seldom been utilized in steganalysis before, are studied. In the rest of thesis study some steganalytic schemes using textural features including the LDP and LTP have been implemented

Steganalysis Embedding Percentage Determination with Learning Vector Quantization

Steganography (stego) is used primarily when the very existence of a communication signal is to be kept covert. Detecting the presence of stego is a very difficult problem which is made even more difficult when the embedding technique is not known. This article presents an investigation of the process and necessary considerations inherent in the development of a new method applied for the detection of hidden data within digital images. We demonstrate the effectiveness of learning vector quantization (LVQ) as a clustering technique which assists in discerning clean or non-stego images from anomalous or stego images. This comparison is conducted using 7 featuresover a small set of 200 observations with varying levels of embedded information from 1% to 10% in increments of 1%. The results demonstrate that LVQ not only more accurately identify when an image contains LSB hidden information when compared to k-means or using just the raw feature sets, but also provides a simple method for determining the percentage of embedding given low information embedding percentages. Abstract ©2006 IEEE

Image statistical frameworks for digital image forensics

The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet personal computers as well as high-speed networks have made a digital image a conventional medium for visual information. Creation, duplication, distribution, or tampering of such a medium can be easily done, which calls for the necessity to be able to trace back the authenticity or history of the medium. Digital image forensics is an emerging research area that aims to resolve the imposed problem and has grown in popularity over the past decade. On the other hand, anti-forensics has emerged over the past few years as a relatively new branch of research, aiming at revealing the weakness of the forensic technology. These two sides of research move digital image forensic technologies to the next higher level. Three major contributions are presented in this dissertation as follows. First, an effective multi-resolution image statistical framework for digital image forensics of passive-blind nature is presented in the frequency domain. The image statistical framework is generated by applying Markovian rake transform to image luminance component. Markovian rake transform is the applications of Markov process to difference arrays which are derived from the quantized block discrete cosine transform 2-D arrays with multiple block sizes. The efficacy and universality of the framework is then evaluated in two major applications of digital image forensics: 1) digital image tampering detection; 2) classification of computer graphics and photographic images. Second, a simple yet effective anti-forensic scheme is proposed, capable of obfuscating double JPEG compression artifacts, which may vital information for image forensics, for instance, digital image tampering detection. Shrink-and-zoom (SAZ) attack, the proposed scheme, is simply based on image resizing and bilinear interpolation. The effectiveness of SAZ has been evaluated over two promising double JPEG compression schemes and the outcome reveals that the proposed scheme is effective, especially in the cases that the first quality factor is lower than the second quality factor. Third, an advanced textural image statistical framework in the spatial domain is proposed, utilizing local binary pattern (LBP) schemes to model local image statistics on various kinds of residual images including higher-order ones. The proposed framework can be implemented either in single- or multi-resolution setting depending on the nature of application of interest. The efficacy of the proposed framework is evaluated on two forensic applications: 1) steganalysis with emphasis on HUGO (Highly Undetectable Steganography), an advanced steganographic scheme embedding hidden data in a content-adaptive manner locally into some image regions which are difficult for modeling image statics; 2) image recapture detection (IRD). The outcomes of the evaluations suggest that the proposed framework is effective, not only for detecting local changes which is in line with the nature of HUGO, but also for detecting global difference (the nature of IRD)

Detecting covert communication channels in raster images

Digital image steganography is a method for hiding secret messages within everyday Internet communication channels. Such covert communications provide protection for communications and exploit the opportunities available in digital media. Digital image steganography makes the nature and content of a message invisible to other users by taking ordinary internet artefacts and using them as cover objects for the messages. In this paper we demonstrate the capability with raster image files and discuss the challenges of detecting such covert communications. The contribution of the research is community awareness of covert communication capability in digital media and the motivation for including such checks in any investigatory analysis

An Artificial Neural Network for Wavelet Steganalysis

Hiding messages in image data, called steganography, is used for both legal and illicit purposes. The detection of hidden messages in image data stored on websites and computers, called steganalysis, is of prime importance to cyber forensics personnel. Automating the detection of hidden messages is a requirement, since the shear amount of image data stored on computers or websites makes it impossible for a person to investigate each image separately. This paper describes research on a prototype software system that automatically classifies an image as having hidden information or not, using a sophisticated artificial neural network (ANN) system. An ANN software package, the ISU ACL NetWorks Toolkit, is trained on a selection of image features that distinguish between stego and nonstego images. The novelty of this ANN is that it is a blind classifier that gives more accurate results than previous systems. It can detect messages hidden using a variety of different types of embedding algorithms. A Graphical User Interface (GUI) combines the ANN, feature selection, and embedding algorithms into a prototype software package that is not currently available to the cyber forensics community