A Study And Analysis Of Watermarking Algorithms For Medical Images

Digital watermarking techniques hide digital data into digital images imperceptibly for different purposes and applications such as copyright protection, authentication, and data hiding. Teknik-teknik pembenaman tera air menyembunyikan data digit ke dalam imej-imej digit untuk pelbagai keperluan dan aplikasi seperti perlindungan hak cipta, pengesahan, dan penyembunyian data

A Study And Analysis Of Watermarking Algorithms For Medical Images

Digital watermarking techniques hide digital data into digital images imperceptibly for different purposes and applications such as copyright protection, authentication, and data hiding. One of the objectives of health care management systems is to securely archive patients’ records. Moreover, these records may require very large media capacity to store, long time to transmit, and consequently incur higher cost. Classical encryption technology is an important tool that can be used to protect data transmitted over computer networks but it does not solve all digital data protection problems

Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique

In this thesis, the effect of feature type and matching method has been analyzed by comparing different combinations of matching method – feature type for copy-move image forgery detection. The results showed an interaction between some of the features and some of the matching methods. Due to the importance of matching process, this thesis focused on improving the matching process by proposing an enhanced block-based copy-move forgery detection pipeline. The proposed pipeline relied on clustering the image blocks into clusters, and then independently performing the matching of the blocks within each cluster which will reduce the time required for matching and increase the true positive ratio (TPR) as well. In order to deploy the proposed pipeline, two combinations of matching method - feature type are considered. In the first case, Zernike Moments (ZMs) were combined with Locality Sensitive Hashing (LSH) and tested on three datasets. The experimental results showed that the proposed pipeline reduced the processing time by 73.05% to 84.70% and enhanced the accuracy of detection by 5.56% to 25.43%. In the second case, Polar Cosine Transform (PCT) was combined with Lexicographical Sort (LS). Although the proposed pipeline could not reduce the processing time, it enhanced the accuracy of detection by 32.46%. The obtained results were statistically analyzed, and it was proven that the proposed pipeline can enhance the accuracy of detection significantly based on the comparison with other two methods