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Improved DWT-SVD-Based Medical Image Watermarking Through Hamming Code and Chaotic Encryption

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Advances in VLSI, Communication, and Signal Processing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 587))

Abstract

This paper presents an efficient watermarking technique to enhance the performance of DWT-SVD-based approach. The method uses well-known error-correcting code (ECC) and chaotic encryption to reduce the channel noise distortion and improve the security of the technique, respectively. In the proposed method, the cover image is transformed by DWT and the sub-bands are selected for embedding the watermarks. Subsequently, the selected sub-bands are further transformed by SVD. The more robust watermark “patient report” and less robust watermark “patient medical image” is embedded into singular values of the selected DWT sub-bands. The use of transform domain techniques along with hamming code ensures that the approach offers more robustness and reliability. Inclusion of chaos-based encryption carries twofold benefits such as obscuring the watermarked content and strengthening the overall security of the projected scheme. The proposed technique offers a significant value of peak signal-to-noise ratio (PSNR), normalized correlation (NC), bit error rate (BER), number of pixel change rate (NPCR), and unified average changing intensity (UCAI) in presence of major attacks. The performance comparisons of our technique with a similar approach show that the proposed technique is superior for nine considered attacks.

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Thakur, S., Singh, A.K., Kumar, B., Ghrera, S.P. (2020). Improved DWT-SVD-Based Medical Image Watermarking Through Hamming Code and Chaotic Encryption. In: Dutta, D., Kar, H., Kumar, C., Bhadauria, V. (eds) Advances in VLSI, Communication, and Signal Processing. Lecture Notes in Electrical Engineering, vol 587. Springer, Singapore. https://doi.org/10.1007/978-981-32-9775-3_80

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  • DOI: https://doi.org/10.1007/978-981-32-9775-3_80

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9774-6

  • Online ISBN: 978-981-32-9775-3

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