Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/51905
Title: Block-Wise Authentication and Recovery Scheme for Medical Images Focusing on Content Complexity
Contributor(s): Tohidi, Faranak (author); Paul, Manoranjan (author); Hooshmandasl, Mohammad Reza (author); Chakraborty, Subrata  (author)orcid ; Pradhan, Biswajeet (author)
Publication Date: 2020
DOI: 10.1007/978-3-030-39770-8_7
Handle Link: https://hdl.handle.net/1959.11/51905
Abstract: 

Digital images are used to transfer most critical data in areas like medical, research, business, military, etc. The images transfer takes place over an unsecured Internet network. Therefore, there is a need for reliable security and protection for these sensitive images. Medical images play an important role in the field of Telemedicine and Tele surgery. Thus, before making any diagnostic decisions and treatments, the authenticity and the integrity of the received medical images need to be verified to avoid misdiagnosis. This paper proposes a block-wise and blind fragile watermarking mechanism for medical image authentication and recovery. By eliminating embedded insignificant data and considering different content complexity for each block during feature extraction and recovery, the capacity of data embedding without loss of quality is increased. This new embedding watermark method can embed a copy of the compressed image inside itself as a watermark to increase the recovered image quality. In our proposed hybrid scheme, the block features are utilized to improve the efficiency of data concealing for authentication and reduce tampering. Therefore, the scheme can achieve better results in terms of the recovered image quality and greater tampering protection, compared with the current schemes.

Publication Type: Conference Publication
Conference Details: PSIVT 2019: 9th Pacific-Rim Symposium on Image and Video Technology Workshops, Sydney, Australia, 18th - 22nd November, 2019
Source of Publication: Image and Video Technology: PSIVT 2019, p. 86-99
Publisher: Springer
Place of Publication: Cham, Switzerland
Fields of Research (FoR) 2020: 460102 Applications in health
461103 Deep learning
460308 Pattern recognition
Socio-Economic Objective (SEO) 2020: 209999 Other health not elsewhere classified
280115 Expanding knowledge in the information and computing sciences
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Publisher/associated links: http://www.psivt.org/psivt2019/program.html
Series Name: Lecture Notes in Computer Science
Series Number : 11994
Appears in Collections:Conference Publication
School of Science and Technology

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