Matlab code for DWT based Watermarking

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Description

Matlab code for DWT based Watermarking

Abstract 

In this manuscript, a stationary wavelet transform-based digital image watermarking algorithm is proposed. The proposed algorithm combines the information of low-frequency DWT coefficients and the watermark video without any change in the information present in the original video. The Key is a combination and is used to extract the watermark. The proposed method will not affect the quality of the image because of no change in the information present in the original video. The simulation results demonstrate the effectiveness of the proposed algorithm.


Introduction:

Digital watermarking has become a promising research area to face the challenges created by the rapid growth in the distribution of digital content over the internet. To prevents misuse of this data Digital watermarking techniques are very useful, In which a Secret message called watermarks which could be a logo or label, is embedded into multimedia data which again could be used for various applications like copyright protection, authentication, and tamper detection, etc. Based on the requirement of the application the watermark is extracted or detected by a detection algorithm to test the condition of the data. This paper presents another approach for watermarking video and extracting it for authentication purposes.


Existing Systems

  • LSB
  • DCT

Drawbacks of Exisitng System

  • High Computational.
  • Less accuracy 
  • Time-consuming

Proposed Method

  • DWT
  • Pre-processing
  • Fusion 

Advantages

  • The DWT Proves to be simple and effective
  • Better quality and minimal error
  • High accuracy 

Block Diagram 

Matlab code for DWT based Watermarking 1
Matlab code for DWT based Watermarking

Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk

Software Requirement

  • MATLAB 2014a

REFERENCES

  • [1] Upadhyay, Y. and Wasson, V. 2014. “Analysis of Liver MR Images for Cancer Detection using Genetic Algorithm”. International Journal of Engineering Research and General Science. Vol.2, No.4, PP: 730-737.
  •  [2] Kumar, P. Bhalerao, S. 2014. “Detection of Tumor in Liver Using Image Segmentation and Registration Technique”. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE). Vo.9, No.2, PP: 110-115.
  •  [3] Selle, D.; Spindler, W.; Preim, B. and Peitgen, H. O. 2000. “Mathematical Methods in Medical Imaging: Analysis of Vascular Structures for Liver Surgery Planning”. PP: 1-21. 
  • [4] Zimmer, C. and Olivo-Marin, J. C. 2005. “Coupled Parametric Active Contours”. Transactions on Pattern Analysis and Machine Intelligence. Vol.27, No.11, PP: 1838-1841.
  •  [5] Chitra, S. and Balakrishnan, G. 2012. “Comparative Study for Two-Color Spaces HSCbCr and YCbCr in Skin Color Detection”. Applied Mathematical Sciences. Vol.6, No.85, PP: 4229 – 4238.

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