Medical Image Tamper Detection Based on Passive Image Authentication
Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient’s history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission. It decreases visual quality of the medical image and can cause false diagnosis. The proposed method uses passive image authentication mechanism to detect the tampered regions on medical images. Structural texture information is obtained from the medical image by using local binary pattern rotation invariant (LBPROT) to make the keypoint extraction techniques more successful. Keypoints on the texture image are obtained with scale invariant feature transform (SIFT). Tampered regions are detected by the method by matching the keypoints. The method improves the keypoint-based passive image authentication mechanism (they do not detect tampering when the smooth region is used for covering an object) by using LBPROT before keypoint extraction because smooth regions also have texture information. Experimental results show that the method detects tampered regions on the medical images even if the forged image has undergone some attacks (Gaussian blurring/additive white Gaussian noise) or the forged regions are scaled/rotated before pasting.
KeywordsCopy move forgery Medical image security LBPROT SIFT Passive image authentication
Compliance with Ethical Standards
The authors declare that they have no conflict of interest.
- 1.Das S, Kundu MK: Effective management of medical information through ROI-lossless fragile image watermarking technique. Computer Methods and Programs in Biomedicine 111(3):662–75, 2013Google Scholar
- 4.Fridrich AJ, Soukal BD, and Lukáš AJ: Detection of copy-move forgery in digital images. Proceedings of Digital Forensic Research Workshop 3(2):652–63, 2003Google Scholar
- 5.Zain JM, Fauzi AM: Medical image watermarking with tamper detection and recovery. Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE 1:3270–3, 2006Google Scholar
- 7.Chiang K-H, Chang-Chien K-C, Chang R-F, Yen H-Y: Tamper detection and restoring system for medical images using wavelet-based reversible data embedding. J. Digit. Imaging. p. 77–90, 2008Google Scholar
- 8.Al-Qershi OM, Khoo BE: Authentication and data hiding using a hybrid ROI-based watermarking scheme for DICOM images. Journal of Digital Imaging 24(1):114–25, 2011Google Scholar
- 9.Al-Qershi OM, Khoo BE: ROI-based tamper detection and recovery for medical images using reversible watermarking technique. 2010 I.E. Int Conf Inf Theory Inf Secur 151–5, 2010Google Scholar
- 10.Liew SC, Zain JM: Reversible medical image watermarking for tamper detection and recovery. Proc - 2010 3rd IEEE Int Conf Comput Sci Inf Technol ICCSIT 2010. p. 417–20, 2010Google Scholar
- 11.Memon NA, Chaudhry A, Ahmad M, Keerio ZA. Hybrid watermarking of medical images for ROI authentication and recovery. International Journal of Computer Mathematics. 2011; 88(10):2057–71.Google Scholar
- 13.Tjokorda Agung BW, Adiwijaya Permana FP: Medical image watermarking with tamper detection and recovery using reversible watermarking with LSB modification and run length encoding (RLE) compression. Proceeding - COMNETSAT 2012 2012 I.E. Int Conf Commun Networks Satell. p. 167–71, 2012Google Scholar
- 14.Deng X, Chen Z, Zeng F, Zhang Y, Mao Y. Authentication and recovery of medical diagnostic image using dual reversible digital watermarking. Journal of nanoscience and nanotechnology. 13(3), 2099–107, 2013.Google Scholar
- 15.Eswaraiah R, Sreenivasa Reddy E: Medical image watermarking technique for accurate tamper detection in ROI and exact recovery of ROI. Int J Telemed Appl. Hindawi Publishing Corporation; 2014; 2014:1–10. Available from: http://www.hindawi.com/journals/ijta/2014/984646/
- 19.Bay H, Tuytelaars T, Van Gool L SURF: speeded up robust features. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). p. 404–17, 2006Google Scholar
- 20.Barre S 1999 Available at: http://www.barre.nom.fr/medical/samples/.
- 22.Thabit R, Khoo BE: Medical image authentication using SLT and IWT schemes. Multimed Tools App 1–24,2015. Available from: doi: 10.1007/s11042–015-3055-x