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On dual encryption with RC6 and combined logistic tent map for grayscale and DICOM

  • 1163: Large-scale multimedia signal processing for security and digital forensics
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Abstract

Sensitive multimedia information of all forms is encrypted, with key, before storage and transmission, to protect from illegal use and data manipulation. Since digital images are larger, it’s crucial to encrypt the content, specifically in medical images effectively. In medical image diagnosis, even a small manipulation of data may lead to misinterpretation. This paper addresses this concern by devising an algorithm suitable for encrypting DICOM and other types of images. RC6 cipher is used for encrypting the approximation coefficients (LL), obtained by applying the Haar wavelet transform on the plain image and combined with the redistributed (confused) detailed coefficients (LH, HL, HH). Generation of keys through governing equations of Combined Logistical Tent map, adds to the robustness of the algorithm against attacks. This algorithm works well for all types of images, including DICOM. Among several image databases available, 30 different modalities of images have been taken for experimentation, and promising results have been achieved. Results show that on an average, for an image of bit-depth eight, the proposed encryption algorithm provides the PSNR of 9.0955 dB, the entropy of 7.9990 bits for an encrypted image and, with a UACI of 33.4549 and NPCR of 99.6129, the algorithm could effectively defy the statistical and differential attacks.

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Acknowledgements

Authors thank the Department of Science & Technology, New Delhi for the FIST funding (SR/FST/ET-II/2018/221). Also, Authors wish to thank the Intrusion Detection Lab at School of Electrical & Electronics Engineering, SASTRA Deemed University for providing infrastructural support to carry out this research work.

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Correspondence to Rengarajan Amirtharajan.

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Manikandan, V., Amirtharajan, R. On dual encryption with RC6 and combined logistic tent map for grayscale and DICOM. Multimed Tools Appl 80, 23511–23540 (2021). https://doi.org/10.1007/s11042-021-10943-8

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