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. It refers that any digital medium can be used as carrier files to embed the secret data. In video steganography, a video file is used as a cover medium within which any secret data 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” gets it origin from the Greek language which means “covered writing”. Steganography studies the exchange of information in a way that the fact of the exchange remains unseen 1. Steganography is the method of concealing secret data into a media transporter such as text, audio, image, and video data in order to build a secret communication between authorized parties. Nowadays, video steganography techniques has found its use in various video sharing and social networking applications such as YouTube, Twitter, and Facebook due to the developments in advanced video over the Internet. The performance measure of any steganography technique depends upon the imperceptibility, hiding capacity, and ability to withstand against attacks.
Digital steganography helps to embed 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 secret message.Containers can be digital pictures, videos, text documents and other digital files. A simplified steganography process 2 is shown in figure 1.
Fig. 1. A simplified steganography process
The two important concept used here is embedding and extracting process. Embedding process is used to hide the secret message in the image as a cover object. A stego key (secret key) is used to embed the message and no one can extract the secret information without processing this key. As in extracting process stego image is obtained that is original image that is holding the secret message. As the key which is used in embedding process, is the same key used in extracting process. Basically encoding is processed at sender side to obtain stego image and decoding at receiver side to obtain secret information.
Steganography can be implemented using two major techniques i.e. Spatial domain and transform domain as described below.
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 type of wavelet transform in which wavelets are discretely sampled. When compared with other different wavelet transforms, a prime advantage that 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.The image on which Haar wavelet is applied gets divided into four band at transform level.The first band is called as ‘approximation coefficient ‘in which low pass filter is applied. The rest 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. The second band has information related to vertical characteristics, whereas the third band shows characteristics in the horizontal direction and the last band contains information about 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 plots an integer data set into another integer data set. This transform is perfectly invertible and yield the original data set.Repeated filter bank algorithm is a one dimensional discrete wavelet transform.The reconstruction includes a convolution with the syntheses filters and the results of these convolutions are added.We first apply one step of the one dimensional transform to all rows in two dimensions. And we repeat the same for all columns.We proceed to the next step with the coefficients that result from a convolution in both directions. Since the integer wavelet transform allows self processing of the resulting components without significant perceptible interaction between them, hence it is expected to make the process of imperceptible embedding more effective.The wavelet filters used have floating point coefficients. So, when the input data consist of sequences of integers (as in the case for images), the resulting filtered outputs does not consist of integers, which doesn’t allow perfect reconstruction of the original image. However, with the introduction of Wavelet transforms that plot integers to integers we are able to identified.
III. LITERATURE SURVEY
There are many 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 divided into two components: the audio and the picture. To work with video steganography, it is important that we understand the audio and image techniques .
This section presents the research work of some 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 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 considered to be 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.
2. Compressed and raw video steganography techniques: a comprehensive survey and analysis 5
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. Both compressed and raw video steganography methods are surveyed. In addition, the main confusion between steganography, cryptography, and watermarking techniques was removed.
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 problem or disadvantage 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 check 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 the author has demonstrated the use of LSB and Random Byte Hiding techniques and MATLAB based implementation is done to find the results.
4. A new approach to video steganography using pixel pattern matching and key segmentation 7.
In this paper the author proposes a video steganography method using pixel pattern matching and key segmentation. In the proposed system, the data is first encrypted using Advanced Encryption Standard and then divided using arithmetic division method. In this approach, the data is stored in the form of divisor, quotient ; remainder. The location key is distributed, encrypted and stored in different frames. 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 remainder. If the system is attacked or hacked the chances of the attacker to analyze or predict the pattern will be very difficult as the secret data is embedded with twofold protection.
5. Lazy Wavelet Transform Based Steganography in Video 8
One of the most commonly used technique is Least Significant Bit steganography. 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. This approach to video steganography uses the visual and the audio components. The lazy wavelet transform is first applied to the frames, and then the data is stored in the coefficients of the visual component. The length till which it is stored is hidden using LSB in the audio part .
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
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.