Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications

Watermarking techniques for genuine fingerprint authentication.

Fingerprints have been used to authenticate people remotely and allow them access to a system. However, the fingerprint-capture sensor is cracked easily using false fingerprint features constructed from a glass surface. Fake fingerprints, which can be easily obtained by attackers, could cheat the system and this issue remains a challenge in fingerprint-based authentication systems. Thus, a mechanism that can validate the originality of fingerprint samples is desired. Watermarking techniques have been used to enhance the fingerprint-based authentication process, however, none of them have been found to satisfy genuine person verification requirements. This thesis focuses on improving the verification of the genuine fingerprint owner using watermarking techniques. Four research issues are being addressed to achieve the main aim of this thesis. The first research task was to embed watermark into fingerprint images collected from different angles. In verification systems, an acquired fingerprint image is compared with another image, which was stored in the database at the time of enrolment. The displacements and rotations of fingerprint images collected from different angles lead to different sets of minutiae. In this case, the fingerprint-based authentication system operates on the ‘close enough’ matching principle between samples and template. A rejection of genuine samples can occur erroneously in such cases. The process of embedding watermarks into fingerprint samples could make this worse by adding spurious minutiae or corrupting correct minutiae. Therefore, a watermarking method for fingerprint images collected from different angles is proposed. Second, embedding high payload of watermark into fingerprint image and preserving the features of the fingerprint from being affected by the embedded watermark is challenging. In this scenario, embedding multiple watermarks that can be used with fingerprint to authenticate the person is proposed. In the developed multi-watermarks schema, two watermark images of high payloads are embedded into fingerprints without significantly affecting minutiae. Third, the robustness of the watermarking approach against image processing operations is important. The implemented fingerprint watermarking algorithms have been proposed to verify the origin of the fingerprint image; however, they are vulnerable to several modes of image operations that can affect the security level of the authentication system. The embedded watermarks, and the fingerprint features that are used subsequently for authentication purposes, can be damaged. Therefore, the current study has evaluated in detail the robustness of the proposed watermarking methods to the most common image operations. Fourth, mobile biometrics are expected to link the genuine user to a claimed identity in ubiquitous applications, which is a great challenge. Touch-based sensors for capturing fingerprints have been incorporated into mobile phones for user identity authentication. However, an individual fake fingerprint cracking the sensor on the iPhone 5S is a warning that biometrics are only a representation of a person, and are not secure. To make thing worse, the ubiquity of mobile devices leaves much room for adversaries to clone, impersonate or fabricate fake biometric identities and/or mobile devices to defraud systems. Therefore, the integration of multiple identifiers for both the capturing device and its owner into one unique entity is proposed

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

RDWT and SVD Based Secure Digital Image Watermarking Using ACM

It is demonstrated via computer simulations that the proposed RDWT, SVD and ACM based digital image watermarking scheme provides better watermark concealment and high robustness against both geometric and image processing attacks. Furthermore, the robustness of the proposed scheme is investigated for different dimensions of the binary watermark logo. It is shown that the robustness of our method is independent of the watermark size

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed