Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks

Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file. In th...

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Autores principales:	Inas Ali Abdulmunem, Eman S. Harba, Hind S. Harba
Formato:	article
Lenguaje:	AR EN
Publicado:	College of Science for Women, University of Baghdad 2021
Materias:	Steganography, Image Stego, Video Stego, Convolutional neural networks, Hiding Data. Science Q
Acceso en línea:	https://doaj.org/article/ba59eb05c3534b13ac3e28abd80c0b21
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spelling	oai:doaj.org-article:ba59eb05c3534b13ac3e28abd80c0b212021-12-04T16:12:53ZAdvanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks10.21123/bsj.2021.18.4.13172078-86652411-7986https://doaj.org/article/ba59eb05c3534b13ac3e28abd80c0b212021-12-01T00:00:00Zhttps://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5112https://doaj.org/toc/2078-8665https://doaj.org/toc/2411-7986 Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file. In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus, the exact way by which the network will hide the information is unable to be known to anyone who does not have the weights. The second goal is to increase hiding capacity, which has been achieved by using CNN as a strategy to make decisions to determine the best areas that are redundant and, as a result, gain more size to be hidden. Furthermore, In the proposed model, CNN is concurrently trained to generate the revealing and hiding processes, and it is designed to work as a pair mainly. This model has a good strategy for the patterns of images, which assists to make decisions to determine which is the parts of the cover image should be redundant, as well as more pixels are hidden there. The CNN implementation can be done by using Keras, along with tensor flow backend. In addition, random RGB images from the "ImageNet dataset" have been used for training the proposed model (About 45000 images of size (256x256)). The proposed model has been trained by CNN using random images taken from the database of ImageNet and can work on images taken from a wide range of sources. By saving space on an image by removing redundant areas, the quantity of hidden data can be raised (improve capacity). Since the weights and model architecture are randomized, the actual method in which the network will hide the data can't be known to anyone who does not have the weights. Furthermore, additional block-shuffling is incorporated as an encryption method to improved security; also, the image enhancement methods are used to improving the output quality. From results, the proposed method has achieved high-security level, high embedding capacity. In addition, the result approves that the system achieves good results in visibility and attacks, in which the proposed method successfully tricks observer and the steganalysis program. Inas Ali AbdulmunemEman S. HarbaHind S. HarbaCollege of Science for Women, University of BaghdadarticleSteganography, Image Stego, Video Stego, Convolutional neural networks, Hiding Data.ScienceQARENBaghdad Science Journal, Vol 18, Iss 4 (2021)
institution	DOAJ
collection	DOAJ
language	AR EN
topic	Steganography, Image Stego, Video Stego, Convolutional neural networks, Hiding Data. Science Q
spellingShingle	Steganography, Image Stego, Video Stego, Convolutional neural networks, Hiding Data. Science Q Inas Ali Abdulmunem Eman S. Harba Hind S. Harba Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
description	Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file. In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus, the exact way by which the network will hide the information is unable to be known to anyone who does not have the weights. The second goal is to increase hiding capacity, which has been achieved by using CNN as a strategy to make decisions to determine the best areas that are redundant and, as a result, gain more size to be hidden. Furthermore, In the proposed model, CNN is concurrently trained to generate the revealing and hiding processes, and it is designed to work as a pair mainly. This model has a good strategy for the patterns of images, which assists to make decisions to determine which is the parts of the cover image should be redundant, as well as more pixels are hidden there. The CNN implementation can be done by using Keras, along with tensor flow backend. In addition, random RGB images from the "ImageNet dataset" have been used for training the proposed model (About 45000 images of size (256x256)). The proposed model has been trained by CNN using random images taken from the database of ImageNet and can work on images taken from a wide range of sources. By saving space on an image by removing redundant areas, the quantity of hidden data can be raised (improve capacity). Since the weights and model architecture are randomized, the actual method in which the network will hide the data can't be known to anyone who does not have the weights. Furthermore, additional block-shuffling is incorporated as an encryption method to improved security; also, the image enhancement methods are used to improving the output quality. From results, the proposed method has achieved high-security level, high embedding capacity. In addition, the result approves that the system achieves good results in visibility and attacks, in which the proposed method successfully tricks observer and the steganalysis program.
format	article
author	Inas Ali Abdulmunem Eman S. Harba Hind S. Harba
author_facet	Inas Ali Abdulmunem Eman S. Harba Hind S. Harba
author_sort	Inas Ali Abdulmunem
title	Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
title_short	Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
title_full	Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
title_fullStr	Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
title_full_unstemmed	Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
title_sort	advanced intelligent data hiding using video stego and convolutional neural networks
publisher	College of Science for Women, University of Baghdad
publishDate	2021
url	https://doaj.org/article/ba59eb05c3534b13ac3e28abd80c0b21
work_keys_str_mv	AT inasaliabdulmunem advancedintelligentdatahidingusingvideostegoandconvolutionalneuralnetworks AT emansharba advancedintelligentdatahidingusingvideostegoandconvolutionalneuralnetworks AT hindsharba advancedintelligentdatahidingusingvideostegoandconvolutionalneuralnetworks
_version_	1718372697789956096

Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks

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