A New Steganography Algorithm Using Hybrid Fuzzy Neural Networks

AbstractIn recent years, image steganography has been one of the emerging research areas. As the field of information technology is advancing, the need of information security is increasing day by day. Steganography is a widely used communication method in today's scenario which involves sending secret information in appropriate carriers. Since it have an interesting property of concealing the message as well as the existence of the message, steganography is on its evolutionary path to unearth new platforms. As the field of steganalysis is growing exponentially, the need of developing strong steganographic algorithms is also growing. Since the use of steganography is spreading across various fields, the goal of increasing the embedding capacity, security and image quality is being major concerns. We propose a new image steganographic method which is based on random selection of pixels for secret data embedding and post processing the stego-image using Hybrid Fuzzy Neural Networks. The pixels where secret data is to be embedded is selected randomly using a pseudo random key. In the selected pixels the last 2 or 3 bits are used for hiding. The resultant degradation in the quality of stego-image is handled by an efficient pixel adjustment process with the use of fuzzy neural networks.. The experimental results reveal that this method can achieve an embedding capacity of 3 bits per byte with excellent stego-image quality and high imperceptibility

StegNet: Mega Image Steganography Capacity with Deep Convolutional Network

Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We~further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.Comment: https://github.com/adamcavendish/StegNet-Mega-Image-Steganography-Capacity-with-Deep-Convolutional-Networ

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

Prototype development for embedding large amount of information using secure LSB and neural based steganography

The security of information became a very important issue. Steganography is an effective way to hide the desired secret information in seemingly innocent cover files which are mostly multimedia files. Using multimedia files as hosts to hide the information in will avoid the need to secure the communication when sending secret messages. The challenge to Steganography is the amount of information to be embedded in the host file without affecting the properties of that file and to avoid distortion of the image, the video, or the sound host file and as a result, to avoid detection of hidden information existence. The need for new methods, techniques and algorithms to make enhancements regarding increasing the amount the hidden information, preserving the host file quality, preserving the size of the file, and keep it robust against steganalysis. To achieve these goals, the embedding must be in suitable locations in the multimedia file, choosing the proper. A recent approach is using artificial intelligence that teaches the machine to give the best candidate bits to hide the information in. This approach is remarkably theoretically efficient, and this approach is the basis of this project to implement a prototype that uses this approach. In this project, for embedding, neural network with adaptive smoothing error back propagation that keeps trying to refine the Stego file until it reaches the best embedding results besides another adaptive Steganography method using concepts called main cases and sub cases. In this project, four layers of security will be used to secure the hidden information and to add more complexity for steganalysis and another point of focus in this project will be on embedding the maximum amount of information that can be embedded without affecting the other objectives