Multimedia Tools and Applications

, Volume 77, Issue 4, pp 4863–4882 | Cite as

A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine

  • Aditi Zear
  • Amit Kumar SinghEmail author
  • Pardeep Kumar


In this paper, an algorithm for multiple watermarking based on discrete wavelet transforms (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) has been proposed for healthcare applications. For identity authentication purpose, the proposed method uses three watermarks in the form of medical Lump image watermark, the doctor signature/identification code and diagnostic information of the patient as the text watermarks. In order to improve the robustness performance of the image watermark, Back Propagation Neural Network (BPNN) is applied to the extracted image watermark to reduce the noise effects on the watermarked image. The security of the image watermark is also enhanced by using Arnold transform before embedding into the cover. Further, the symptom and signature text watermarks are also encoded by lossless arithmetic compression technique and Hamming error correction code respectively. The compressed and encoded text watermark is then embedded into the cover image. Experimental results are obtained by varying the gain factor, different sizes of text watermarks and the different cover image modalities. The results are provided to illustrate that the proposed method is able to withstand a different of signal processing attacks and has been found to be giving excellent performance for robustness, imperceptibility, capacity and security simultaneously. The robustness performance of the method is also compared with other reported techniques. Finally, the visual quality of the watermarked image is evaluated by the subjective method also. This shows that the visual quality of the watermarked images is acceptable for diagnosis at different gain factors. Therefore the proposed method may find potential application in prevention of patient identity theft in healthcare applications.


Multimedia data Discrete wavelet transforms Discrete cosine transform Singular value decomposition Back propagation neural network Healthcare 



The Author’s are sincerely thankful to the potential/ anonymous reviewer’s for their critical comments and suggestions to improve the quality of the paper.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringJaypee University of Information Technology WaknaghatSolanIndia

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