Design of Video Steganography using IWT Algorithm Priya Dubey1

April 16, 2019 Critical Thinking

Design of Video Steganography using IWT Algorithm

Priya Dubey1 , Sharon Mamootil2 ,Sherin Jacob3 ,Nancy Jeyakumar4,Smita Rukhande5
1,2,3,4,5 Department of Information Technology,Mumbai University
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]

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Abstract— Steganography is a technique for embedding digital information inside another digital medium such as text, images, audio or video, without revealing its presence in the medium. Thus it refers that any digital medium can be used as carrier files to embed the secret data. In video steganography, a video file will be used as a cover medium within which any secret message can be embedded. In steganography, the secret information can be hidden either directly by altering the pixel values of the images in the spatial domain or in the frequency components of the images after transforming the images into frequency domain by using transformation algorithms such as DCT (Discrete Cosine Transform), DWT(Discrete Wavelet Transform) and IWT (Integer Wavelet Transform). In proposed system, secret data is embedded inside a video file using Transform domain IWT for enhancing data security. The performance of any steganography method, relies on the imperceptibility, hiding capacity, and robustness against attacks. Therefore the proposed system aims at achieving better results against attack.

Keywords— Steganography, Video Steganography, Spatial Domain Steganography, Frequency Domain Steganography, DCT, DWT

I . INTRODUCTION
The word “steganography” comes from the Greek language and means “covered writing”. Steganography as a science studies the exchange of information in a way that the fact of the exchange remains unseen 1. Steganography is the art of concealing secret data in a particular interactive media transporter such as text, audio, image, and video data in order to build a covert communication between authorized parties. Nowadays, video steganography techniques are important in many video sharing and social networking applications such as Livestreaming, YouTube, Twitter, and Facebook because of noteworthy developments in advanced video over the Internet. The performance of any steganography method, ultimately, relies on the imperceptibility, hiding capacity, and robustness against attacks.
Digital steganography embeds the message (a sequence of bits) into a container (another sequence of bits), receiving a stego container as a result – a sequence of bits, similar to the original container, but containing the hidden message. Digital pictures, videos, text documents and other digital files can be used as a container. A simplified steganography process 2 is shown in figure 1.

Fig. 1. A simplified steganography process

The two main concept used here is embedding and extracting process. Embedding process is used hide the secret message in the image as a cover object. A stego key is used to embed the message and no one can extract the information without processing this key. As in extracting process stego image is obtained that is actual image that is holding the secret message. As the key is used in embedding process it is also used in extracting process. Basically encoding is done at sender side to obtain stego image and decoding at receiver side to obtain secret information .

II. TECHNIQUES
Steganography can be implemented using two major techniques i.e. Spatial domain and transform domain as described below.

A.Spatial Domain
It is based on manipulation of pixel of the image.In this domain, cover image and secret data is modified using LSB and Level Encoding. Initially the cover image is decomposed into bit planes and then LSB of bit planes is replaced with secret data. LSB substitution method is most used steganography technique. This substitution technique involves embedding the data at the minimum weighting bit as it will not affect the value of original pixel. LSB substitution provides better quality of image, but the only disadvantage with it is the simplicity of its extraction process. Thus, an intelligent hacker can easily extract the data that has been sent. For an 8-bit image, the least significant bit i.e. The 8th bit of each byte of the Image will be changed by the 1-bit of secret message. For 24 bit image, the colors of each component like RGB (red, green and blue) will be changed The Spatial Domain based methods are popular due to high embedding capacity but these are highly vulnerable to attacks like image filters, rotation, cropping and scaling.

B. Transform Domain Steganography
Unlike the spatial domain technique the transform domain technique , instead of hiding the secret message directly in the pixels, embeds the messages into the frequency coefficients of the image. For this, mathematical transformations such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Integer Wavelet transform(IWT) are applied to the image to transform it into frequency components. After transformation,the secret message is hidden in the frequency coefficients.Security can be improved by hiding the data in selected frequency coefficients based on some threshold value. Then the image will be transformed back into spatial domain by inverse transformation.In the transform domain algorithm, a true color image is transformed into IWT (Integer Wavelet Transform) domain using a wavelet called ‘haar’ wavelet. The wavelet transforms the image into four frequency bands, namely AC, HC, VC, and DC. The band AC is the approximation coefficient band and the other three are detail coefficients. The secret data are embedded in the DC component and the image is transformed back into original form by reverse transformation.

B.1 Discrete Cosine Transform
The DCT can be used to convert an image from the spatial domain into the frequency domain.The DCT separates parts of an image based on frequency. As the Image signal energy is stored in low-frequency regions, therefore high-frequency information can be removed or manipulated without causing signi?cant distortion of image quality.The approaches that operate in the transform domain generally use properties of the DCT.
LSB manipulation cannot be applied to the colours of pixels when working with lossy compression formats such as JPEGs. This is because JPEG images use a DCT as part of the compression process, during which values such as LSBs are not necessarily retained. Whilst the conversion between the spatial and the transform domain (and vice versa) uses lossy compression, the discrete cosine coe?cients are stored using lossless encoding, therefore most JPEG steganography techniques encode data in the discrete cosine coe?cients.

B.2 Discrete Wavelet Transform (DWT)
The discrete wavelet transform (DWT) is a wavelet transform in which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information (location in time). Discrete wavelet transform (DWT) are applied to discrete data sets and produce discrete outputs.
Discrete wavelet transform maps data from the time domain (the original or input data vector) to the wavelet domain. An image that undergoes Haar wavelet transform will be divided into four bands at each of the transform level. The first band is called as ‘approximation coefficient ‘where low pass filter is applied. The other three bands are called ‘details’ where high pass filter is applied. These bands contain directional characteristics. The size of each of the bands is also compressed to half. Specifically, the second band contains vertical characteristics, the third band shows characteristics in the horizontal direction and the last band represents diagonal characteristics of the input image. Each pixel in an image that will go through the wavelet transform computation will be used only once and no pixel overlapping during the computation.

B.3 Integer Wavelet Transform
Integer to integer wavelet transforms maps an integer data set into another integer data set. This transform is perfectly invertible and yield exactly the original data set. A one dimensional discrete wavelet transform is a repeated filter bank algorithm. The reconstruction involves a convolution with the syntheses filters and the results of these convolutions are added. In two dimensions, we first apply one step of the one dimensional transform to all rows. Then, we repeat the same for all columns. In the next step, we proceed with the coefficients that result from a convolution in both directions. Since the integer wavelet transform allows independent processing of the resulting components without significant perceptible interaction between them, hence it is expected to make the process of imperceptible embedding more effective. However, the used wavelet filters have floating point coefficients. Thus, when the input data consist of sequences of integers (as in the case for images), the resulting filtered outputs no longer consist of integers, which doesn’t allow perfect reconstruction of the original image. However, with the introduction of Wavelet transforms that map integers to integers we are able to characterize.

III. LITERATURE SURVEY
There are numerous techniques for hiding data in a digital container ?le. Although this project focuses solely on using a video container ?le, there are techniques in audio and image steganography that still bear relevance to video ?le formats. Furthermore, video can be split into two components: the audio stream and the picture stream. To be able to work with video steganography, it is important that we understand the audio and image (picture) techniques that have already been developed and explored within digital steganography.
This section presents the research work of some prominent authors in the same field and explains a short description of various techniques used for video compression as well as embedding.

1. An Approach Towards Image, Audio and Video Steganography 4
In this paper authors demonstrated the use of steganography in such a way that the video intended to be encoded is segmented into frames. Each frame of the video is considered to be a single RGB image. The frames are then converted into respective number of sound files. Later the steganographed files (sound files) are decrypted and combined in the original sequence to retrieve back the video using the reverse procedure. Again ordinary sound files containing speech and music were also tried to encode into a RGB image, which was later retrieved by running the decoding procedure.

2. Compressed and raw video steganography techniques: a comprehensive survey and analysis 5
This paper presents a comprehensive study and analysis of numerous cutting edge video steganography methods and their performance evaluations from literature. Both compressed and raw video steganography methods are surveyed. In addition, the main confusion between steganography, cryptography, and watermarking techniques was eradicated.

3. Steganography over Video File using Random Byte Hiding and LSB Technique 6
In this paper, the hidden message is text and it is implemented over video file. The traditional well known method uses image as cover which has the limitation of embedding dimension. So, cover should be a video to overcome the limitation of embedding dimension. Nowadays, the use of a video based steganography is common and numbers of steganalysis tools are available to check whether the video is stego-video or not. Most of the tools are checking for information hided by LSB, DCT, Frequency Domain Analysis etc and finds whether the video has hidden or secret data or not. In this paper, LSB and Random Byte Hiding techniques are implemented and MATLAB based implementation is done to simulate the results.

4. A new approach to video steganography using pixel pattern matching and key segmentation 7.
This paper proposes a video steganography algorithm using pixel pattern matching and key segmentation. In the proposed system, the data is encrypted using Advanced Encryption Standard and divided using arithmetic division method. In this approach, the data will be stored in the form of divisor, quotient ; amp; remainder. The location key is also distributed, encrypted and stored in different frames. Along with this pixel, pattern matching is also used to avoid distortion of the video frame. This system will be difficult to crack since the location key is divided as well as encrypted and stored in different video frames along with this the secret message is stored in the form of a quotient, a divisor and a remainder. Even if the system is attacked the chance of the intruder to predict the pattern will be difficult as the secret data is embedded with dual protection.

5. Lazy Wavelet Transform Based Steganography in Video 8
The most commonly used technique is Least Significant Bit steganography (LSB steganography). But instead of traditional LSB encoding, a modified encoding technique will be used which will first transform the video using a Lazy Lifting Wavelet transform and then apply LSB in the sub-bands of the video that has been obtained. The proposed approach to video steganography utilizes the visual as well as the audio component. The lazy wavelet transform is applied to the visual frames, and the data is stored in the coefficients of the visual component. The length up to which it is stored is hidden using LSB in the audio component.

IV. PROPOSED METHOD
The objective of the proposed video steganography system is to enhance the security,increase the psnr ratio , decrease the MSE , decrease the BER and enhance robustness of the secret communication. The system aims to utilize the IWT algorithm for efficient data hiding. The different process involved are :
A. Preprocessing of cover-video image: The cover video file is divided into frames using built in functions in matlab.
B. Encoding Process: A frame is selected from the video file and is transformed by haar wavelet. The haar wavelet transforms the frame into four frequency bands namely AC, HC, VC and DC .Each band is a copy of the original image but in different frequency level which provides a certain amount of energy. The first band ,AC, is approximation band which represents the image filtered with a low pass filter .The other three bands, HC,VC,DC, are called ‘details’ where high pass filter is applied. These bands contain directional characteristics. The secret message is then embedded in the frequency coefficients of DC band using LSB substitution method. Once the embedding is done the frame is merged back with the other frames using inverse integer wavelet transform to get the stego video.The stego video is then sent to
the receiver through various means.

Figure.2 Data embedding in the Iwt domain

C.Decoding Process: Stego video is then again applied with the IWT algorithm to retrieve the message from the dc
component of the frame.

Figure.3 Data decoding in the Iwt domain

V. CONCLUSION
The proposed video steganography algorithm aims for secured data transmission with imperceptible distortions in the resulting AVI videos. It also intends to extract the data without any loss in quality and size of the original video files. The algorithm aims to provides better security, high psn ratio,low MSE with less or equal distortions in test videos. To further improve on the video steganography method, future revisions include hiding multiple data at the same time and hiding different types of secret data in different types of video files without disguising the quality of the video files.

VI.ACKNOWLEDGMENT
Authors would like to thank Head of the Department Dr. Hari K Chavan , Principal Dr. S. M. Khot, FCRIT, Vashi for their kind support and suggestions. We would also like to extend our sincere thanks to all the faculty members of the department of Information Technology engineering and colleagues for their help.

REFERENCES

E. Zielinska, W. Mazurczyk and K. Szczypiorski, “Trends in Steganography,” Communications of the ACM, no. 03, issue 57, 2014, pp. 86–95
I. Cox, M. Miller, J. Bloom, J.Fridrich, andT. Kalker. “Digital Watermarking and Steganography (Second Edition)”, Morgan Kaufmann Publishers, ISBN: 978-0-12- 372585-1, 2007.
R. J. Mstafa and K. M. Elleithy, “A New Video Steganography Algorithm Based on the Multiple Object Tracking and Hamming Codes,” in IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 335-340, 2015.
Kunal Hossain and Ranjan Parekh, “An Approach Towards Image, Audio and Video Steganography” , 978-1-5090-1047-9/16/$31.00 ©2016 IEEE
Mstafa, R. J., ; Elleithy, K. M., ” Compressed and raw video steganography techniques: a comprehensive survey and analysis”, Multimedia Tools and Applications, 76(20), 21749–21786, 2016 Springer.
Bhole, A. T., ; Patel, R, “Steganography over video file using Random Byte Hiding and LSB technique”, IEEE International Conference on Computational Intelligence and Computing Research 2012.
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K., Rora, K. K., Singh, K., ; Verma, S. “Lazy Wavelet Transform Based Steganography in Video”. International Conference on Communication Systems and Network Technologies, 2013.

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