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

Abstract

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.