Robust watermarking for magnetic resonance images with automatic region of interest detection

Medical image watermarking requires special considerations compared to ordinary watermarking methods. The first issue is the detection of an important area of the image called the Region of Interest (ROI) prior to starting the watermarking process. Most existing ROI detection procedures use manual-based methods, while in automated methods the robustness against intentional or unintentional attacks has not been considered extensively. The second issue is the robustness of the embedded watermark against different attacks. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this thesis addresses these issues of having automatic ROI detection for magnetic resonance images that are robust against attacks particularly the salt and pepper noise and designing a new watermarking method that can withstand high density salt and pepper noise. In the ROI detection part, combinations of several algorithms such as morphological reconstruction, adaptive thresholding and labelling are utilized. The noise-filtering algorithm and window size correction block are then introduced for further enhancement. The performance of the proposed ROI detection is evaluated by computing the Comparative Accuracy (CA). In the watermarking part, a combination of spatial method, channel coding and noise filtering schemes are used to increase the robustness against salt and pepper noise. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER). Based on experiments, the CA under eight different attacks (speckle noise, average filter, median filter, Wiener filter, Gaussian filter, sharpening filter, motion, and salt and pepper noise) is between 97.8% and 100%. The CA under different densities of salt and pepper noise (10%-90%) is in the range of 75.13% to 98.99%. In the watermarking part, the performance of the proposed method under different densities of salt and pepper noise measured by total PSNR, ROI PSNR, total SSIM and ROI SSIM has improved in the ranges of 3.48-23.03 (dB), 3.5-23.05 (dB), 0-0.4620 and 0-0.5335 to 21.75-42.08 (dB), 20.55-40.83 (dB), 0.5775-0.8874 and 0.4104-0.9742 respectively. In addition, the BER is reduced to the range of 0.02% to 41.7%. To conclude, the proposed method has managed to significantly improve the performance of existing medical image watermarking methods

Efficiency of LSB steganography on medical information

The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder

An overview of multi-filters for eliminating impulse noise for digital images

An image through the digitization process is referred to as a digital image. The quality of the digital image may be degenerating due to interferences on the acquisition, transmission, extraction, etc. This attracted the attention of many researchers to study the causes of damage to the information in the image. In addition to finding cause of image damage, the researchers also looking for ways to overcome this problem. There are many filtering techniques that have been introduced to deal the damage to the information in the image. In addition to eliminating noise from the image, filtering techniques also aims to maintain the originality of the features in the image. Among the many research papers on image filtering there is a lack of review papers which are an important to facilitate researchers in understanding the differences in each filtering technique. Additionally, it helps researchers determine the direction of research conducted based on the results of previous research. Therefore, this paper presents a review of several filtering techniques that have been developed so far

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy