%0 Journal Article %1 noauthororeditor %A Han, Baoru %D 2020 %J IJIRIS:: International Journal of Innovative Research in Information Security %K cover cryptography image security steganography %N Issue VIII %P 76-80 %R https://doi.org/10.26562/ijiris.2020.v0707.002 %T A DIFFERENCE PERCEPTUAL HASHING ALGORITHM FOR MEDICAL VOLUME DATA AGAINST LOCAL NONLINEAR GEOMETRIC ATTACKS %U http://www.ijiris.com/volumes/Vol7/iss-7/02.AUIS10081.pdf %V Volume VII %X Traditional image hashing methods can often only resist traditional global geometric attacks and cannot resist local nonlinear geometric attacks. This has a huge impact on the protection, identification and authentication of three-dimensional medical volume data. In response to these problems, a new difference perceptual hashing algorithm for medical volume data is proposed in this paper. It uses the difference between adjacent elements in each column of the volume data feature matrix to generate a hashing sequence. Experiments show that it has a good ability to resist local nonlinear geometric attacks.

@article{noauthororeditor, abstract = {Traditional image hashing methods can often only resist traditional global geometric attacks and cannot resist local nonlinear geometric attacks. This has a huge impact on the protection, identification and authentication of three-dimensional medical volume data. In response to these problems, a new difference perceptual hashing algorithm for medical volume data is proposed in this paper. It uses the difference between adjacent elements in each column of the volume data feature matrix to generate a hashing sequence. Experiments show that it has a good ability to resist local nonlinear geometric attacks.}, added-at = {2020-09-01T17:18:08.000+0200}, author = {Han, Baoru}, biburl = {https://www.bibsonomy.org/bibtex/227e07017ca62d7452ce7985ac3096b67/ijiris}, description = {With the progress of communication technology, particularly the wide application of Internet, more and more medical images are transmitted in the public network [1-2]. How to protect the patient's personal information, medical image retrieval, identification and authentication, become more and more urgent [3-4]. The research of perceptual hashing is based on image watermarking technology, and also refers to the basic theory of traditional cryptography hashing and multimedia authentication [5-6]. It has become a hot spot in the related research fields of multimedia processing and security [7]. Perceptual hashing can transform multimedia data into shorter bit sequences. Perceptual hashing provides a reliable technical guarantee for the protection, identification and authentication of multimedia digital content. Perceptual hashing can be applied to medical images [8]. As the medical imaging equipment advance, most medical images used in hospitals are three-dimensional medical volume data, so it is of great significance to study the perceptual hashing of medical volume data. However, in practical applications, there is usually a type of geometric attacks, which belong to local nonlinear geometric attacks. At present, there are few perceptual algorithms that can resist local nonlinear geometric attacks, so it is of great significance to study perceptual hashing algorithms against local nonlinear geometric attacks. In order to solve this problem, a new difference hashing algorithm is proposed to resist local nonlinear geometric attacks. Experimental results prove that the difference hashing algorithm has strong ability to resist local nonlinear geometric attacks.}, doi = {https://doi.org/10.26562/ijiris.2020.v0707.002}, interhash = {e075e066c8813178910a0b3f0f49ee87}, intrahash = {27e07017ca62d7452ce7985ac3096b67}, issn = {2349-7017}, journal = {IJIRIS:: International Journal of Innovative Research in Information Security}, keywords = {cover cryptography image security steganography}, language = {English}, month = {August 2020}, note = {1. Seenivasagam V, Velumani R, “A QR code based zero-watermarking scheme for authentication of medical images in teleradiology cloud,” Computational and mathematical methods in medicine, 2013. 2. Parah S A , Sheikh J A , Ahad F , et al, “Information hiding in medical images: a robust medical image watermarking system for E-healthcare, “Multimedia Tools & Applications, vol. 76,no.8, pp.1-35.,2017 3. Aparna, Puvvadi , and P. V. V. Kishore , “Biometric-based efficient medical image watermarking in E-healthcare application,”IET Image Processing,vol .13,no.3 , pp.421-428,2019. 4. Aparna, Puvvadi , and P. V. V. Kishore , “An iris biometric-based dual encryption technique for medical image in e-healthcare application,” International Journal of Computational Vision and Robotics, vol.10,no.1,2020,:. 5. Raul R C, Claudia F U, Trinidad-Bias G J, “ Data Hiding Scheme for Medical Images,” Electronics, Communications and Computers, 2007. CONIELECOMP '07. 17th International Conference on. IEEE, pp.32-32,2007. 6. Singh A K, Kumar B, Dave M, et al , “Robust and imperceptible dual watermarking for telemedicine applications,” Wireless Personal Communications, vol.80,no.4, pp.1415-1433,2015 7. Aparna, Puvvadi , and P. V. V. Kishore , “A blind medical image watermarking for secure e-healthcare application using crypto-watermarking system,” Journal of Intelligent Systems ,2019. 8. Singh A K, Kumar B, Dave M, et al, “ Multiple watermarking on medical images using selective discrete wavelet transform coefficients,” Journal of Medical Imaging & Health Informatics, vol 5,no.3, pp.607-614,2015. 9. Ghouti, Lahouari , “ Robust perceptual color image hashing using randomized hypercomplex matrix factorizations,” Multimedia Tools and Applications ,vol77,no15,pp:19895-19929,2018 10. Cui, Yan , et al, “Supervised discrete discriminant hashing for image retrieval,” Pattern Recognition ,vol78,pp:79-90,2018}, number = {Issue VIII}, pages = {76-80}, timestamp = {2020-09-01T17:18:08.000+0200}, title = {A DIFFERENCE PERCEPTUAL HASHING ALGORITHM FOR MEDICAL VOLUME DATA AGAINST LOCAL NONLINEAR GEOMETRIC ATTACKS}, url = {http://www.ijiris.com/volumes/Vol7/iss-7/02.AUIS10081.pdf}, volume = {Volume VII}, year = 2020 }