Fragile watermarking scheme based on SHA-256 hash function and mersenne twister for medical image authentication

Medical images can be easily manipulated by irresponsible persons and the altered medical image can be hard to identify. Fragile watermarking scheme is an alternative solution to authenticate and protect the medical images. Fragile watermarking scheme becomes vulnerable against modification by attackers. This research proposed a fragile watermarking scheme for medical images based onSHA-256 and Mersenne twister. A medical image was split into a region of interest (ROI) and region of non-interest (RONI). The ROI as watermarked image is encrypted by SHA-256 and the result is scrambled by Arnold transform with a secret key before embedding the watermark. The scrambled hash values are randomly embedded into RONI by using Mersenne Twister with a secret key. The experimental results showed that our scheme produces high imperceptibility with PSNR value of about 83 dB. The proposed scheme was able to detect tampers accurately on the medical images. The proposed scheme improved the invisibility of the watermarked image and it provided additional security. The proposed scheme authenticated and validated the originality of the medical images

Video steganography based on DCT psychovisual and object motion

Steganography is a technique of concealing the message in multimedia data. Multimedia data, such as videos are often compressed to reduce the storage for limited bandwidth. The video provides additional hidden-space in the object motion of image sequences. This research proposes a video steganography scheme based on object motion and DCT-psychovisual for concealing the message. The proposed hiding technique embeds a secret message along the object motion of the video frames. Motion analysis is used to determine the embedding regions. The proposed scheme selects six DCT coefficients in the middle frequency using DCT-psychovisual effects of hiding messages. A message is embedded by modifying middle DCT coefficients using the proposed algorithm. The middle frequencies have a large hiding capacity and it relatively does not give significant effect to the video reconstruction. The performance of the proposed video steganography is evaluated in terms of video quality and robustness against MPEG compression. The experimental results produce minimum distortion of the video quality. Our scheme produces a robust of hiding messages against MPEG-4 compression with average NC value of 0.94. The proposed video steganography achieves less perceptual distortion to human eyes and it's resistant against reducing video storage

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

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