Medical Image Watermarking Based on SIFT-DCT Perceptual Hashing

Medical image containing patient information is often faced with various attacks in the transmission process. In order to enhance the medical information system security, and effectively solve the problem of medical data protection, a new algorithm of medical image watermarking based on SIFT-DCT perceptual hashing (scale invariant feature transform and discrete cosine transform) is proposed. Firstly, use SIFT-DCT perceptual hashing to extract features for the original medical images and quantize to generate hashing sequences. Then, use chaotic maps to encrypt the watermarking and embed it in the medical image. Finally, calculate the correlation coefficients of the embedded and extracted watermarking sequences to reflect the robustness of the algorithm. The results of experiment show that the proposed algorithm has good robustness against conventional attacks and geometric attacks, especially in terms of rotation, translation and clipping.

[1]  Sotiris Pavlopoulos,et al.  Multiple Image Watermarking Applied to Health Information Management , 2006, IEEE Transactions on Information Technology in Biomedicine.

[2]  Z. Jane Wang,et al.  Perceptual Image Hashing Based on Shape Contexts and Local Feature Points , 2012, IEEE Transactions on Information Forensics and Security.

[3]  Lu Fang,et al.  Beyond SIFT using binary features in Loop Closure Detection , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[4]  Heung-Kyu Lee,et al.  Robust DT-CWT Watermarking for DIBR 3D Images , 2012, IEEE Transactions on Broadcasting.

[5]  Guoyou Wang,et al.  Robust Scale-Invariant Feature Matching for Remote Sensing Image Registration , 2009, IEEE Geoscience and Remote Sensing Letters.

[6]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[7]  Yanqiang Lei,et al.  Robust image hash in Radon transform domain for authentication , 2011, Signal Process. Image Commun..

[8]  Arijit Sur,et al.  SIFT based robust image watermarking resistant to resolution scaling , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[9]  Vishal Monga,et al.  Robust and Secure Image Hashing via Non-Negative Matrix Factorizations , 2007, IEEE Transactions on Information Forensics and Security.

[10]  Chun Kiat Tan,et al.  Security Protection of DICOM Medical Images Using Dual-Layer Reversible Watermarking with Tamper Detection Capability , 2011, Journal of Digital Imaging.

[11]  Ruey-Feng Chang,et al.  Tamper Detection and Recovery for Medical Images Using Near-lossless Information Hiding Technique , 2008, Journal of Digital Imaging.

[12]  Ramarathnam Venkatesan,et al.  Robust perceptual image hashing via matrix invariants , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[13]  Vishal Monga,et al.  Perceptual Image Hashing Via Feature Points: Performance Evaluation and Tradeoffs , 2006, IEEE Transactions on Image Processing.

[14]  Jinguang Sun,et al.  Geometrical Attack Robust Spatial Digital Watermarking Based on Improved SIFT , 2010, 2010 International Conference on Innovative Computing and Communication and 2010 Asia-Pacific Conference on Information Technology and Ocean Engineering.