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

Computational intelligence in steganalysis environment

This paper presents gives a consolidated view of digital media steganalysis from the perspective of computational intelligence (CI). The environment of digital media steganalysis can be divided into three (3)domains which are image steganalysis, audio steganalysis, and video steganalysis. Three (3) major methods have also been identified in the computational intelligence based on these steganalysis domains which are bayesian, neural network, and genetic algorithm. Each of these methods has pros and cons. Therefore, it depends on the steganalyst to use and choose a suitable method based on their purposes and its environment

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

PIRANHA: an engine for a methodology of detecting covert communication via image-based steganography

In current cutting-edge steganalysis research, model-building and machine learning has been utilized to detect steganography. However, these models are computationally and cognitively cumbersome, and are specifically and exactly targeted to attack one and only one type of steganography. The model built and utilized in this thesis has shown capability in detecting a class or family of steganography, while also demonstrating that it is viable to construct a minimalist model for steganalysis. The notion of detecting steganographic primitives or families is one that has not been discussed in literature, and would serve well as a first-pass steganographic detection methodology. The model built here serves this end well, and it must be kept in mind that the model presented is posited to work as a front-end broad-pass filter for some of the more computationally advanced and directed stganalytic algorithms currently in use. This thesis attempts to convey a view of steganography and steganalysis in a manner more utilitarian and immediately useful to everyday scenarios. This is vastly different from a good many publications that treat the topic as one relegated only to cloak-and-dagger information passing. The subsequent view of steganography as primarily a communications tool useable by petty information brokers and the like directs the text and helps ensure that the notion of steganography as a digital dead-drop box is abandoned in favor of a more grounded approach. As such, the model presented underperforms specialized models that have been presented in current literature, but also makes use of a large image sample space (747 images) as well as images that are contextually diverse and representative of those seen in wide use. In future applications by either law-enforcement or corporate officials, it is hoped that the model presented in this thesis can aid in rapid and targeted responses without causing undue strain upon an eventual human operator. As such, a design constraint that was utilized for this research favored a False Negative as opposed to a False Positive - this methodology helps to ensure that, in the event of an alert, it is worthwhile to apply a more directed attack against the flagged image

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

Creación de una herramienta para la generación de analizadores esteganográficos para imágenes : JUBSAC (Java Universal Blind StegAnalyzer Creator)

La Esteganografía, es la ciencia que estudia las técnicas de ocultación de información u objetos dentro de otros, llamados portadores, de modo que no se perciba su existencia. Es una mezcla de artes y técnicas que se combinan para conformar la práctica de ocultar y enviar información sensible en un portador que pueda pasar desapercibido. El Estegoanálisis, es la ciencia que estudia las técnicas que se usan para detectar y/o anular información oculta por la esteganografía. Al igual que con la esteganografía, es considerada una mezcla entre técnicas y arte para descubrir la información oculta. El proyecto presentado en la siguiente memoria, trata sobre la realización de un programa capaz de crear analizadores o detectores esteganográficos para imágenes. Estos detectores, también llamados estegoanalizadores, son programas capaces de detectar la presencia de información oculta en archivos digitales de imágenes. Se ha construido un procedimiento, por el cual se puede estegoanalizar cualquier algoritmo esteganográfico a través de una serie de atributos que se extraen de las imágenes. Se ha implementado este esquema procedimental, en una aplicación. Esta aplicación, la cual ha sido llamada JUBSAC, las siglas en inglés de Creador de EstegoAnalizadores Universales a Ciegas en Java (Java Universal Blind StegAnalyzer Creator). El principal objetivo de JUBSAC es la experimentación e investigación de nuevos métodos estegoanalíticos mediante técnicas de inteligencia artificial. Si bien hay otras propuestas para el estegoanálisis mediante el uso de técnicas de inteligencia artificial, no sé ha abordado hasta la fecha un marco general para la realización de este tipo de procedimientos experimentales. Para comprobar la eficacia de la herramienta desarrollada se han realizado una serie de experimentos sobre un algoritmo esteganográfico. JUBSAC ha permitido agilizar y simplificar el proceso gracias al uso de la inteligencia artificial. Se ha utilizado el mejor analizador, con un 100% de eficacia, resultante de los experimentos, para construir una implementación de un estegoanalizador para ser usado directamente desde JUBSAC. Por lo tanto, JUBSAC puede ser usada, además de para la experimentación, para detectar información oculta en imágenes.Ingeniería en Informátic

Neural network based steganalysis in still images

Seganalysis has recently attracted researchers ’ interests with the development of information hiding techniques. In this paper we propose a new method based neural network to get statistics features of images to identify the underlying hidden data. We first extract features of image embedded information, then input them into neural network to get output. And experiment results indicate this method is valid in steganalysis. This method will be used for internet/network security, watermarking and so on