A Novel Method for Forgery Detection on Lung Cancer Images

Year 2022, Volume: 11 Issue: 3, 13 - 20, 30.09.2022

Bilgehan Gürünlü , Serkan Öztürk

Abstract

With the increase in lung cancer cases in recent years, rapid advances have been made in imaging technologies for lung cancer detection. Thanks to these advances in image processing and medicine, more successful disease diagnosis is achieved. On the other hand, the security of these images is one of the issues that are overlooked or little thought about in this field. The security of images such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) is as important as disease detection. Manipulations such as cyber attacks, commit insurance forensic and destruction of evidence can be carried out on health images for various purposes. This problem is included in the study area of both image processing and information security. In this study, we developed a new image forgery detection method based on Center Symmetric Local Binary Pattern texture extraction algorithm, which has not been used on lung cancer images before as far as we known. We tested this method that we have developed on a very up-to-date lung cancer image data set. Although the success of the method is the first study, it is satisfactory. The experimental results of the proposed method show that our method can be used in this field.

Keywords

information security, lung cancer images, computed tomography, passive image security.

References

• Gurunlu, B., Ozturk, S., “A Survey on Photo Forgery Detection Methods”, ITM Web of Conferences CMES 2018, vol.22, pp.1-5, 2018.
• Mirsky, Yisroel, et al. “CT-GAN: Malicious Tampering of 3D Medical Imagery Using Deep Learning.” 28th {USENIX} Security Symposium ({USENIX} Security 19), 2019.
• Vincent Christlein, Christian Riess, Johannes Jordan, Corinna Riess, Elli Angelopoulou. “An Evaluation of Popular Copy-Move Forgery Detection Approaches"; IEEE Transactions on Information Forensics and Security (TIFS) 2012
• Fridrich, J., Soukal, D., Lukáš, J., “Detection of Copy-Move Forgery in Digital Images”. Proceedings of DFRWS 2003, Cleveland, USA, 2003.
• P. Yadav and Y. Rathore, “Detection of Copy-Move Forgery of Images Using Discrete Wavelet Transform,” Int. J. Comput. Sci. Eng., vol. 4, no. 4, pp. 565–570, 2012.
• Junwen, Wang, Liu Guangjie, Zhangqin Zhan, Dai Yuewei and Wang Zhi-quan. “Fast and Robust Forensics for Image Region-duplication Forgery.” (2009).
• Bayram, S, Sencar, HT & Memon, N, An efficient and robust method for detecting copy-move forgery. IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009.
• Ojala, T., Pietikainen, M., Harwood, D., "A Comparative Study of Texture Measures with Classification Based on Feature Distributions,", Pattern Recognition ,29:51-59, 1996.
• Heikkilä M., Pietikäinen M., Schmid C. Description of Interest Regions with Center-Symmetric Local Binary Patterns. In: Kalra P.K., Peleg S. (eds) Computer Vision, Graphics and Image Processing, 2006
• Open-Acess Medical Image Repositories, URL: https://www.aylward.org/notes/open-access-medical-image-repositories (Visited on May 15, 2022).