Live Video Watermarking Using LabVIEW 1Dr.M. Senthamil Selvi,

2J. Angel Ida Chellam and

3P.V. Kavitha

1,2,3Department of Information Technology,

Sri Ramakrishna Engineering College, Coimbatore,

Tamil Nadu, India. 1senthamilselvi@srec.ac.in,

2angel.anbuseelan@srec.ac.in,

3kavitha.krishna@srec.ac.in

Abstract The use of digital media applications, and copyright protection has

obtained tremendous importance. Digital Watermarking is a technology

used for the copyright protection of digital applications. A comprehensive

approach for watermarking digital video is introduced. Hybrid digital

video watermarking scheme based on Discrete Wavelet Transform (DWT)

and Discrete Cosine Transform (DCT) has been proposed. Discrete Cosine

Transform (DCT) with Principal Component Analysis (PCA) helps in

reducing correlation among the wavelet coefficients. Wavelet

decomposition of each video frame is done thereby dispersing the

watermark bits into the uncorrelated coefficients. The video frames are first

decomposed using DWT. The binary watermark is embedded in the low

frequency wavelet coefficients. The imperceptible high bit rate watermark

embedded is robust against various attacks that can be carried out on the

watermarked video, such as filtering, contrast adjustment, noise addition

and geometric attacks.

Key Words:Video watermarking, DWT, DCT, PCA.

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1. Introduction

The rapid growth of Internet and networks technique, multimedia data

transforming and sharing is common in today’s world. Multimedia data is easily

copied and modified, so necessity for Copyright protection is increasing. Digital

Watermarking has been proposed as technique for Copyright protection of

multimedia data. The process of Digital Watermarking involves embedding and

extraction of watermarked data in order to provide security. The embedding

method must leave the original data perceptually un-changed, yet should impose

modifications which can be detected by using an appropriate extraction

algorithm. The digital content could be any data that the user likes to protect.

The watermark is mainly used to authenticate the owner to ensure copyright

protection.

Digital watermarking refers to embedding of watermarks into a digital content.

However a technique named DWT method for digital video watermarking is

divided in two parts; they are Embedding watermark and Extracting watermark.

This presents a new image encryption algorithm, which can improve the

security of image during transmission more effectively. As a result, it’s

important for creators and distributors to protect their copyright and ownership

of their digital media. In this background, watermarking technique is an

effective method to solve the problem, and it has been widely used in the

copyright protection. Now the digital video watermarking technique has become

the focus of the theoretical research and practical application. Many schemes

have achieved good results both in security and robustness. However, some

practical technical problems have not been a better solution. Many schemes are

at the cost of complex theory and large computational quantity in order to

obtain a better robust scheme, which is difficult to meet the real-time

requirement, such as the broadcast monitoring, digital television system, etc.

2. Proposed System

The proposed system is mainly constructed to protect the data when transmitted

through a public network and ensure the authentication of both the data and the

user. The proposed system comprises of six modules. They are:

Login

Camera Settings and Data Input

Video Recording

Embedding Watermark

Extracting Watermark

Mail

Each module has their specific role where in each module the role are as

respectively, Login ensures authentication of the user, the user will be able to

select the required camera according to their need and input the patients detail

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and provide a file path to save the video, once the settings are made

appropriately the video is recorded and the video gets embedded with the

inputted data by using DWT in accordance with PCA, after embedding the next

process is extraction where the patients details are extracted and displayed in the

output panel using DCT and finally a screenshot of the video is mailed to the

respected recipient.

Fig 2.1.1: Methodology of the proposed System

Video Watermark Embedding Process

Steps involved in watermark embedding process:

The Real time Video is captured and its various properties are measured. All the video frames are extracted using LabVIEW programming.

Absolute difference between the successive frames is calculated, where fi (x,y) is the present image and fi+1(x,y) is the next successive image.

The difference images are processed over morphological filter to reduce the noise.

The area/region of the moving object is clearly noticed, marks it as M1 and coverts into Binary Images.

𝑥 ′

𝑦 ′ =

11

11

𝑥

𝑦 𝑚𝑜𝑑(𝑁)

Where 𝑥 ′

𝑦 ′ is the Arnold Transform, and the input image

𝑥

𝑦 and N is

the size of the image

The Static area/region in the video frames is marked as S1 and 2level of DWT is applied. The static area/region is divided into LL, LH, HL and

HH, the diagonal elements LH and HL have less information content

than LL and HH so these two regions are marked as S´1,1 and S´1,2.

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The “H” shape of the scrambled logo is embedded in morphological filter frames M´1 and 2-level of DWT is embedded in S´1,1 and S´1,2

2-level Inverse DWT is applied and the frames are re- converted into a video which contains the watermark.

Fig 2.1.1: Embedding Watermark

Watermarking Extraction Process

Steps involved in Watermarking Recovery process:

In the process of extracting the watermark from the video, embedded video is converted into frames and static and dynamic frames are

obtained.

The difference /moving frames are converted to binary form and morphological filter is performed and marked as M’1 and the Static

frames as S’1.

The “H” shape of scrambled data is scanned from the region M’1 and the Watermarked Logo extracted from S’ 1, 1 & S’ 1, 2.

Anti-Arnold transform is applied to the frames and 2- level DWT and Inverse DWT is applied to S’1, 1 and S’1, 2 as above process.

The watermark logo/Image is processed from each video frame separately and checked with various types of attacks and the robustness

is calculated.

The video is extracted from the recovery process vice versa as the embedding process, and the block diagram is shown in the Fig.2.2.1

Various attacks are performed and MSE and PSNR values are calculated.

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Fig 2.2.1: Extraction of Watermark

3. Techniques Used

DCT (Discrete Cosine Transform)

X= DCT (video/audio input)

Returns the discrete cosine transform of the data input

Can be referred to as the even part of the Fourier series

Converts an image or audio block into it’s equivalent frequency coefficients

Concept

The DCT transform of an image brings out a set of numbers called coefficients.

A coefficient’s usefulness is determined by its variance.

If the coefficient of the frames have large variance then the quality of the image is lost.

Fig 3.1: DCT

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The image is broken into 8x8 groups, each containing 64 pixels. Three of these

8x8 groups are enlarged in this figure, showing the values of the individual

pixels, a single byte value between 0 and 255. [7]

Applications

DVD/Video CD Players

Cable TV

DBS Systems

HDTV Disadvantages of DCT

Only spatial correlation of the same block is considered whereas the neighboring block is neglected.

High compression ratios have very low bit rates

DCT function is fixed

Transmission and storage of uncompressed video would be extremely costly and impractical

It contains a large number of byte information DWT (Discrete Wavelet Theorem) Equations

1-D wavelet transform

wavelet transform converts an input series x0, x1, ..xm, into one high-pass

wavelet coefficient series and one low-pass wavelet coefficient series (of length

n/2 each)

where sm(Z) and tm(Z) are called wavelet filters, K is the length of the filter, and i=0, ..., [n/2]-1.

In practice,such transformation will be applied recursively on the low-pass series until the desired number of iterations is reached

DWT for an image

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Fig 3.2.1: DWT

Advantages

No blocking effect

It has good localization both in time and spatial frequency domain.

Transformation of the whole image introduces inherent scaling

Interpretation of data is easy Adaptive FPS(Frames per Second)

It is a technique which is mainly used to maintain the quality of the video.

When the video is run on the system that is been used its gets adapted to it and

does not provide any distortion or overlapping of frames. The fps value is made

dynamic where the rate of distortion is self-corrected. The distortion rate is

justified through histogram.

4. Experimental Result

The proposed system is mainly implemented in order to authenticate the data

when transmitted the data over a public network. The system comprises of

embedding and extraction of watermark, where the frames are divided with the

lowest frequency coefficients and the watermark is embedded is based on the

quality of the video. In order to maintain the quality of the video without any

distortion from the cover and watermarked video a technique called adaptive

FPS is used. It helps in retaining the quality of the video and makes camera

settings adjustable to the system being used. Since a hybrid algorithm is used

here the disadvantages of both the DCT and DWT is overcome and provides a

reliable system. When the data is transmitted over a public network the

watermark is extracted by inversing the embedding process. The comparison of

existing and proposed system is given below:

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Table 4.1: Comparison

5. Conclusion and Future Work

A text watermark is embedded over a real time video as and when the video is

recorded. This enhances authentication and copyright protection when the video

is transferred over a public network. It is found that imperceptibility is high and

number of pixels that can be embedded in the video is also high. It is robust

against various attacks. An adaptive FPS technique is used to automatically

adjust the settings of the camera according to the system used. Correlations of

the frame among wavelet coefficients are reduced and PSNR metric is enhanced

to maintain the quality of the video. An adaptive FPS technique is used to

automatically adjust the settings of the camera according to the system used.

The pattern recognition is made easier and determines how to use an image into

a computer vision application, through object detection and the use of other

functionalities of LabView suggest that the use of LabView as an excellent

platform to develop robotic projects as well as vision and image processing

applications. The main disadvantage of the proposed system is that it occupies a

large space since the number of bits to embed is higher, it can be resolved by

either minimizing the bits or by developing an efficient algorithm. Security

measures can be enhanced such as identifying video cracking attacks.

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