Design Of Video Steganography Using Iwt Algorithm

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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 has to be used as a cover medium within which any type of secret message can be embedded.The secret information can be hidden either directly 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) or by altering the pixel values of the images in the spatial domain.

This paper aims to embed secret data inside a video file using both the methods, spatial and frequency, and the outcomes are compared and analyzed on the basis of the parameters such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), BER (Bit Error Rate) and standard Deviation.

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


The Greek word “steganography” means “covered writing”. Steganography studies the exchange of information in a way such that the fact of the exchange remains unseen .  Steganography is a method of hiding the secret data in an 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 Live streaming, YouTube, Twitter, and Facebook because of noteworthy developments in advanced video over the Internet.

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 The performance of the steganography method relies on the 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.

The two main actions used here is embedding and extracting process. The Embedding process is used to hide the secret message in the image . A stego key can be used to embed the secret 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. The encoding is always done at sender side to obtain stego image and decoding at receiver side to obtain secret message.


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

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. An intelligent hacker can easily extract the data that has been sent. For any 8-bit image, the least significant bit that is the 8th bit of every 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.

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.

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.

Discrete Wavelet Transform (DWT)

The discrete wavelet transform (DWT) is a wavelet transform in which the wavelets are discretely sampled. The key advantage is temporal resolution: it captures both frequency and location information (location in time).The DWT are applied to discrete data sets and it produces discrete outputs.

DWT is responsible to map the data to the wavelet domain from time domain. An image undergoing the 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. On the other three band high pass filter is applied and they are also called as called details .These bands contain directional characteristics. The size of all the bands are compressed to half. The second band contains vertical characteristics, the third band contains the horizontal characteristics and the last band represents diagonal characteristics of an input image. Every pixel in an image that will go through the computation will be used only once and no pixel overlapping during the computation.

Integer Wavelet Transform

Integer to integer wavelet transforms maps an integer data set into another integer data set. This transform is perfectly invertible and it yields exactly the original data set. 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 dimension, we first apply one step of the one dimensional transform to all the rows. In next step, we repeat the same for all columns. Further, we proceed with the coefficients that result from a convolution in both directions. Since the integer wavelet transform is the one that allows independent processing of the resulting components without significant perceptible interaction between them, it is expected to make the process of imperceptible embedding more effective. However, the used wavelet filters have floating point coefficients so when the input data consist of sequences of integers (for example consider images), the resulting filtered outputs no longer consist of integers, which instead doesn’t allow perfect reconstruction of the original image. However, with the initiation of Wavelet transforms that map the integers to integers we can characterize.

Related Work

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.

37338001831975This 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. In this paper author has demonstrated the use of steganography in such a way that the video intended to be encoded is divided into frames. Each frame of the video is a single RGB image. The frames is then converted into number of sound files. Later the sound files are decrypted and combined in the original sequence to retrieve back the video using the reverse technique. 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[4].

In this paper the author has provided a comprehensive study and analysis of many cutting edge video steganography methods and their performance evaluations from literature[5].In this paper the author has performed steganography over video file using Random Byte Hiding and LSB Technique[6]. In this paper the author proposes a video steganography method using pixel pattern matching and key segmentation where the data is first encrypted using Advanced Encryption Standard and then divided using arithmetic division method. and the data is stored in the form of divisor, quotient & remainder.

The location key is distributed, encrypted and stored in different frames[7]. In this paper an encoding technique is used which first changes the video using a Lazy Lifting Wavelet transform and then adds the LSB technique in the subparts of the video that has been obtained[8].In this paper, a new approach is provided ,where the secret message characters are replaced by image pixels positions, based on a predefined the pixel color value and character-color mapping, then embedding these pixels positions to three sets of pixels selected from the image based on the component value difference between adjacent pixels, for each RGB channel, red, green and blue respectively[9].

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.

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.

Decoding Process

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


The proposed video steganography algorithm aims to embed secret data inside a video file using both the methods, spatial and frequency, and the outcomes are analyzed and compared on the basis of parameters such as increase in PSNR (Peak Signal to Noise Ratio), low MSE (Mean Square Error),and low BER (Bit Error Rate) for secure data transmission with imperceptible distortions in order to extract the data without any loss in quality and size of the original video files. 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.


  1. E. Zielinska, W. Mazurczyk and K. Szczypiorski, “Trends in Steganography,” Communications of the ACM, no. 03, issue 57, 2014, pp. 86–95
  2. 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.
  3. 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
  4. Kunal Hossain and Ranjan Parekh, “An Approach Towards Image, Audio and Video Steganography” , 978-1-5090-1047-9/16/$31.00 ©2016 IEEE.
  5. 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.
  6. 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
  7. Wadek r, H., Babu, A., Bharvadia, V., & Tatwadarshi, P. N. “A new approach to video steganography using pixel pattern matching and key segmentation”.International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)2017.
  8. Patel, K, Rora, K. K., Singh, K., & Verma, S. “Lazy Wavelet Transform Based Steganography in Video”. International Conference on Communication Systems and Network Technologies, 2013
  9. Omari, Z. Y., & Al-Taani, A. T. (2017). “Secure LSB steganography for colored images using character-color mapping.” 2017 8th International Conference on Information and Communication Systems (ICICS). doi:10.1109/iacs.2017.7921954

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Design Of Video Steganography Using Iwt Algorithm. (2019, Nov 24). Retrieved from

Design Of Video Steganography Using Iwt Algorithm
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