Copy-move forgery detection of medical images using golden ball optimization

Abstract

The medical images can be tampered with by attackers with a malevolent goal of hiding or creating multiple copies of lesions, resulting in wrong treatment, false insurance claim, defame public figures, and so on. Such forgery demands authentication of digital images before starting the diagnosis and treatment of the patients. The existing key-point (KP) based forgery detection methods may not uniformly distribute the KPs on the images, thereby making the detection process to fail for images with forgeries in smooth regions. This paper attempts to employ a minimum Eigen-value algorithm that distributes the KPs in the entire image region and apply speeded up robust features and singular value decomposition for obtaining reduced descriptors at identified KPs. Besides, the method applies golden ball-based optimization, inspired by the behaviour of players in team-based sports games, for optimal clustering of the evaluated features. This paper studies the performances of the suggested method on 300 medical images and compares the results with the existing methods. It exhibits that the precision, specificity, sensitivity, and accuracy of the developed method are superior to the existing methods. Though the suggested method outperforms, it can further be improved by combining it with block-based methods.

KEYWORDS:

• Copy-move forgery
• forgery detection in medical images
• golden ball optimization
• SURF descriptors
• K-means clustering

Acknowledgements

The authors thankfully acknowledge the administrative officers of Annamalai University for the computing and internet facilities provided to perform this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

D. Suganya

Mrs D. Suganya received the B.E Degree in Electrical and Electronics Engineering and M. E Degree in Computer Science and Engineering from Annamalai University, in 2002 and 2009, respectively. She is presently pursuing Ph.D (CSE) at Annamalai University. Her research interests are  image processing, evolutionary algorithms and soft computing tools.

K. Thirunadana Sikamani

Dr K. Thirunadana Sikamani, Professor and Head of CSE, obtained his B.E. degree in ECE from Mookambikai College of Engineering, Trichy in 1989, M.E degree in CSE from Regional Engineering College, Trichy in 1997 and Ph.D degree in CSE Manonmaniam Sundaranar University, Tirunelveli in 2014. He has more than 30 years of teaching and 10 years of research experience. His research specialization is to meet the challenges in Mobile Adhoc Networks, Image Processing, Wireless networks and Cloud Computing.

J. Sasikala

Dr J. Sasikala received the B.E. Degree in Electronics and Communication Engineering from Madras University, India in 1993, and the M.E and Ph. D degrees in Computer Science and Engineering from Annamalai University in 2005 and 2011, respectively. She has been working as an Associate Professor in the Department of Information Technology, Annamalai University, Tamil Nadu, India, since 1999. She is specialized  in the area of optimization, evolutionary algorithms, and image processing.