Jasni Mohamad Zain

Medical Image Watermarking with Tamper Detection and Recovery

This paper discussed security of medical images and reviewed some work done regarding them. A fragile watermarking scheme was then proposed that could detect tamper and subsequently recover the image. Our scheme required a secret key and a public chaotic mixing algorithm to embed and recover a tampered image. The scheme was also resilient to VQ attack. The purposes were to verify the integrity and authenticity of medical images. We used 800times600times8 bits ultrasound (US) greyscale images in our experiment. We tested our algorithm for up to 50% tampered block and obtained 100% recovery for spread-tampered block

A wavelet-based particle swarm optimization algorithm for digital image watermarking

Digital watermarking techniques have been proposed for copyright protection and multimedia data authentication. The achieved tradeoffs from these techniques between imperceptibility and robustness are always controversial. This paper proposes the application of Discrete Wavelet Transform DWT into image watermarking by using Particle Swarm Optimization PSO which is an evolutionary technique with the stochastic, population-based algorithm for solving this problem. To protect copyright information of digital images, the original image is decomposed according to two-dimensional discrete wavelet transform. Subsequently the preprocessed watermark with an affined scrambling transform is optimal embedded into the vertical subband HL_{m} coefficients in wavelet domain without compromising the quality of the image. In the optimal process, the scaling factors are trained with the assistance of PSO. Furthermore, a novel algorithmic framework is proposed via a forecasted feasibility of the approach to parameters evaluation of hypothesized watermarked images in DWT domain. Simulation results show that the proposed watermarking procedure has better performances in imperceptibility and robustness under various distortions in the comparison of the previous scheme.

A new efficient normal parameter reduction algorithm of soft sets

Kong et al. [Kong, Z., Gao, L., Wang, L., and Li, S., The normal parameter reduction of soft sets and its algorithm, Computers and Mathematics with Applications 56 (12) (2008) 3029-3037] introduced the definition of normal parameter reduction in soft sets and presented a heuristic algorithm of normal parameter reduction. However, the algorithm is hard to understand and involves a great amount of computation. In this paper, firstly, we give some new related definitions and proved theorems of normal parameter reduction. Then we propose a new efficient normal parameter reduction algorithm of soft sets based on the oriented-parameter sum, which can be carried out without parameter important degree and decision partition. The comparison result on a dataset shows that the proposed algorithm involves relatively less computation and is easier to implement and understand as compared with the algorithm of normal parameter reduction proposed by Kong et al.

Reversible watermarking for authentication of DICOM images

We propose a watermarking scheme that can recover the original image from the watermarked one. The purpose is to verify the integrity and authenticity of DICOM images. We used 800x600x8 bits ultrasound (US) images in our experiment. SHA-256 of the whole image is embedded in the least significant bits of the RONI (Region of Non-Interest). If the image has not been altered, the watermark will be extracted and the original image will be recovered. SHA-256 of the recovered image will be compared with the extracted watermark for authentication.

Robust Image Watermarking Theories and Techniques: A Review

Over the past several decades, digital information science has emerged to seek answers to the question: can any technique ensure tamper-resistance and protect the copyright of digital contents by storing, transmitting and processing information encoded in systems where digital content can easily be disseminated through communication channels? Today it is understood that the answer is yes. This paper reviews the theoretical analysis and performance investigation of representative watermarking systems in transform domains and geometric invariant regions. Digital watermarking is a technology of embedding watermark with intellectual property rights into images, videos, audios, and other multimedia data by a certain algorithm. The basic characteristics of digital watermark are imperceptibility, capacity, robustness and false positive of watermarking algorithm and security of the hiding place. Moreover, it is concluded that various attacks operators are used for the assessment of watermarking systems, which supplies an automated and fair analysis of substantial watermarking methods for chosen application areas.

A novel soft set approach in selecting clustering attribute

Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data. One of the techniques of data clustering was performed by introducing a clustering attribute. Soft set theory, initiated by Molodtsov in 1999, is a new general mathematical tool for dealing with uncertainties. In this paper, we define a soft set model on the equivalence classes of an information system, which can be easily applied in obtaining approximate sets of rough sets. Furthermore, we use it to select a clustering attribute for categorical datasets and a heuristic algorithm is presented. Experiment results on fifteen UCI benchmark datasets showed that the proposed approach provides a faster decision in selecting a clustering attribute as compared with maximum dependency attributes (MDAs) approach up to 14.84%. Furthermore, MDA and NSS have a good scalability i.e. the executing time of both algorithms tends to increase linearly as the number of instances and attributes are increased, respectively.

Reversible medical image watermarking for tamper detection and recovery

This research paper discussed the usage of watermarking in medical images to ensure the authenticity and integrity of the image and reviewed some watermarking schemes that had been developed. A design of a reversible tamper detection and recovery watermarking scheme was then proposed. The watermarking scheme uses a 640x480x8 bits ultrasound grayscale image as a sample. The concept of ROI (Region Of Interest) and RONI (Region Of Non Interest) were applied. Watermark embedded can be used to detect tampering and recovery of the image can be done. The watermark is also reversible.

Evaluation of Medical Image Watermarking with Tamper Detection and Recovery (AW-TDR)

This paper will study and evaluate watermarking technique by Zain and Fauzi. Recommendations will then be made to enhance the technique especially in the aspect of recovery or reconstruction rate for medical images. A proposal will also be made for a better distribution of watermark to minimize the distortion of the region of interest (ROI). The final proposal will enhance AW-TDR in three aspects; firstly the image quality in the ROI will be improved as the maximum change is only 2 bits in every 4 pixels, or embedding rate of 0.5 bits/pixel. Secondly the recovery rate will also be better since the recovery bits are located outside the region of interest. The disadvantage in this is that, only manipulation done in the ROI will be detected. Thirdly the quality of the reconstructed image will be enhanced since the average of 2 x 2 pixels would be used to reconstruct the tampered image.

Data filling approach of soft sets under incomplete information

Incomplete information in a soft set restricts the usage of the soft set. To make the incomplete soft set more useful, in this paper, we propose a data filling approach for incomplete soft set in which missing data is filled in terms of the association degree between the parameters when stronger association exists between the parameters or in terms of the probability of objects appearing in the mapping sets of parameters when no stronger association exists between the parameters. An illustrative example is employed to show the feasibility and validity of our approach in practical applications.

CLUSTERING HIGH DIMENSIONAL DATA USING SUBSPACE AND PROJECTED CLUSTERING ALGORITHMS

Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace clustering and projected clustering are research areas for clustering in high dimensional spaces. In this research we experiment three clustering oriented algorithms, PROCLUS, P3C and STATPC. Results: In general, PROCLUS performs better in terms of time of calculation and produced the least number of un-clustered data while STATPC outperforms PROCLUS and P3C in the accuracy of both cluster points and relevant attributes found. Conclusions/Recommendations: In this study, we analyze in detail the properties of different data clustering method.