STEGANOGRAPHY: IMAGE DATA ENCRYPTION BY USING DEEP NEURAL NETWORK

Abstract

Over the last twenty years, steganography has attracted in the consideration of numerous information security specialists who analyzes and identifies possible threats of information security. In contrast to encryption, which aims to secure the message, steganography requires that a public message or image hides the fact of containing any confidential data. Usually in standard methods of hiding information, the hidden message is encoded in the least significant bit or bits. In the last decade, different efforts have been made utilizing machine learning approaches to stow away data without compromising the astuteness of the layer that transports data. Images are frequently utilized as portion of communication since they are simpler to understand than just a lot of routine text. Users can communicate over compromised networks, in which case information security is essential. Subsequently, an effective method of hidding information without alerting potential attacker about hidden information is essential to guarantee the security of the data transmitted. In my thesis, I focus on the research in image data steganography, which hides a confidential (secret) image in another (cover) image. A deep convolutional neural network with a pixel permutation is used as a hiding network, and various training schemes are evaluated to estimate impact of the secret and cover image similarity on the efficiency of retrieval. My main goal is to provide a method of image encryption that uses machine learning and encryption technology.