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Identity protection is an indispensable feature of any information security system. An identity can exist in the form of digitally written signatures, biometric information, logos, etc. It serves the vital purpose of the owners' verification and provides them with a safety net against their imposters, so its protection is essential. Numerous security mechanisms are being developed to achieve this goal, and information embedding is prominent among all. It consists of cryptography, steganography, and watermarking; collectively, they are known as data hiding (DH) techniques. In addition to providing insight into various DH techniques, this review prominently covers the image watermarking works that have positively influenced its relevant research area. To that end, one of the main aspects of this study is its inclusive nature in reviewing watermarking techniques, via which it aims to provide a 360 ∘ view of the watermarking technology. The main contributions of this study are summarised below. The proposed study covers more than 100 major watermarking works that have positively influenced the field and continue to do so. This approach makes the discussion effective as it allows us to pivot on the vital watermarking works that have positively influenced the research area instead of just highlighting as many existing methods as possible. Moreover, it also empowers us to provide the readers with an insight into the current research trends, the pros and cons of the state-of-the-art methods, and recommendations for future works. In addition to reviewing the state-of-the-art watermarking works, this study solves the issue of reverse-engineering the main existing watermarking methods. For instance, most recent surveys have focused primarily on reviewing as many watermarking works as possible without probing into the actual working of the techniques. This approach can leave the readership without a vital understanding of implementing or reverse-engineering a watermarking method. This issue is especially prevalent among newcomers to the watermarking field; hence, this study presents the breakdown of the well-known watermarking techniques. A new systematisation of classifying existing watermarking methods is proposed. It classifies watermarking techniques into two phases. The first phase divides watermarking methods into three categories based on the domain employed during watermark embedding. The methods are further classified based on other watermarking attributes in the following phase. [ABSTRACT FROM AUTHOR]

Recently, forensic tools have been presented for detecting the forged image and illegal image manipulations. This paper presents an efficient and simple image contents verifying approach acting as a forensic technique. The presented approach mechanism is built by adding a hidden mark in a secret images, this mark achieves image integrity verification and detecting the tampering or forgery in the secret/authentic images. The secret/authentic/authentic image is divided to two main partitions, each portion is segmented to small blocks. These blocks in one partition are used to mark the blocks of the second partition using data transform technique. Firstly, the sensitive image is marked according to a self-embedding method. Then, a transform domain is utilized in order to embed a block-based signature into another block of the same image. Common discrete transform domains like DWT, DCT, and DFT are examined individually. Different analyses and comparison measurements are employed. The DCT is proved to be the most suitable and efficient transform domain to be used with the proposed scheme. At the receiver side, the reverse process is performed to verify image integrity. As proved from the experiments, this mark-algorithm is not visible or observable and robust against various attacks. [ABSTRACT FROM AUTHOR]

This paper addresses a model to secrete the information of one image under another without losing quality of image. Different approaches have been utilized for image hiding as needed, but multiple images maintain secrecy with information under another image is a challenging task. Thus, the framework is proposed to sustain the secrecy of an original image from another image. The proposed system collects random images through ImageNet and uses them as per the requirements of secrete images. The framework is used the deep neural networks method to build secrete information of multiple images under a single image. The enormous transfer of images is used to select standard image modifications using advanced deep learning approaches. It develops the significance of the critical framework that alleviates the choice of finding the hidden image information. Two vital methods such as Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) are used to find out difference between host and secret image by their corresponding evaluation scores. It produces the confidentiality of the image with the help of the host image. Therefore, data from several images are protected under a single image. The different image data are experimented with good performance. For comparative analysis, the accuracy is better in retrieving two secrete images on all experiments, like approximate accuracy is 100%. Still, when we considered PSNR and SSIM scores on the same two secrete images, accuracy became less than 50%. [ABSTRACT FROM AUTHOR]

Social media platforms and news organisations alike are struggling with identifying and combating visual mis/disinformation presented to their audiences. Such processes are complicated due to the enormous number of media items being produced, how quickly media items spread, and the often-subtle or sometimes invisible-to-the-naked-eye nature of deceptive edits. Despite knowing little about the provenance and veracity of the visual content they encounter, journalists have to quickly determine whether to re-publish or amplify this content, with few tools and little time available to assist them in such an evaluation. With the goal of equipping journalists with the mechanisms, skills, and knowledge to be effective gatekeepers and stewards of the public trust, this study reviews current journalistic image verification practices, examines a number of existing and emerging image verification technologies that could be deployed or adapted to aid in this endeavour, and identifies the strengths and limitations of the most promising extant technical approaches. While oriented towards practical and achievable steps in combating visual mis/disinformation, the study also contributes to discussions on fact-checking, source-checking, verification, debunking and journalism training and education. [ABSTRACT FROM AUTHOR]

The scheme is planned to recognize the lost person or any former individual person based on the condition of the user. The lost person can be recognized robotically without human interference by utilizing the artificial intelligence algorithms and deep face sections. The scheme will accumulate the figures from varies camera and kept in the server, the figures that accumulated at the interval of acquiescent will be matched to the figures that taken from the camera by using the predefined algorithms and the precise match may found. After the match found nearby alarm will be alerted then Short Message Service will be directed to the matching mobile numbers worldwide. [ABSTRACT FROM AUTHOR]

Background: Technology has been integrated into our everyday lives and it has become the most prominent part of our daily activities. The COVID-19 pandemic has made the technology most prominent in daily life like grocery shopping, and health consultations and it also made the schools and colleges encourage online learning. Over the past years, online learning has gained a lot of popularity. But the verification and monitoring of the students have always been the important challenges in online examinations. Furthermore, it has to be ensured that the students have completed all the activities without any inappropriate behavior. Objectives: Online Learning/Examinations have gained a great demand due to the everyday circumstances we have been facing. But the credibility of the exams has not been guaranteed due to lack of authentication and other reasons. Methods: To overcome these issues, there is a specific solution based on an automatic proctoring system. This solution consists of the features that help to resolve the primary concerns at hand. The system allows the student to log in and authenticate him/her with the image verification during the examination. It allows the student to take the exam, check the results, and report the problems if any occurred. The main features of the system are the maintenance of logs of window events whenever the user changes tabs or opens a new tab, detection of more than one person in the exam, prevention of the cut, copy, paste, and so on. Conclusions: The main aim of the system is to offer an authentication identity service for students through continuous monitoring. To avoid the fluctuations, due to the changes in the postures of the student, more models can be introduced as future work. [ABSTRACT FROM AUTHOR]

In everyday life the chair is essential for every individual. In the places like house, office and hospitals, the chair is using for long duration. The proposed system is a IoT technology enabled multi-purpose chair, that can be used in home, office and hospitals by implanting/attaching the health sensors like AD8232-Ecg sensor, BP sensor, LM35 temperature sensor, veneir blood pressure sensor, and weight sensor to the node MCU micro-controller together with the thing speak cloud platform and IFTTT technology. With this the office head can know the duration of the hours of his employee that who is sitting in chair. Similarly the doctors can know the immediate health conditions of the patients. Similarly the person who is sitting on the chair can control the home appliances from on chair itself. In this proposed system will get both the local and global alerts with the help of the buzzers and the SMS. This system will lead to monitor the employees, patients and appliances remotely. Background: The invention relates to sending the message to the doctors, whenever there patient gets abnormal conditions of their health. The conditions may be Blood Pressure, Sugar level, Fever, Symptoms of Heart Attack; ECG etc. simultaneously by this proposed chair can control the home/office appliance also. This can be used in home, office and hospitals. Objectives: 1. To Design and develop the chair that can automatically control the home appliances/office appliances locally and globally. 2.To design and develop the chair that can detect the abnormal condition of the person. 3.To design and develop the chair that can calculate the time of the person sitting on the chair. 4.To send the SMS alert globally to the registered doctors/any others, if the person sitting on the chair is in abnormal conditions. 5. Give the local alert system with the help of alarm within that house or office. Methods: The proposed system will immediately detect the health condition of the users that who sit on the chair with the help of buzzer and SMS. Similarly the proposed system facilitates the user to control home appliances locally and remotely. It will help the users to get prescribed by the doctor remotely and can control the home appliances by sitting on the chair itself and remotely with the help of a mobile phone. Conclusions: Accordingly a novel IoT enabled multi-purpose chair will facilitate the users to control the home appliances locally and globally similarly, it detects the abnormal state of the health condition, of the users local and globally and finds the duration of the time that the person sitting on the chair for monitoring purpose. [ABSTRACT FROM AUTHOR]

Purpose: Verification of patient position through pretreatment setup imaging is crucial in modern radiation therapy. As treatment complexity increases and technology evolves, physicist-physician collaboration becomes imperative for safe and successful radiation delivery. Despite the importance of both, residency programs lack formal interprofessional education (IPE) activities or structured training for image verification. Here we show the impact of an interprofessional image verification workshop for residents in a multi-institutional setting.Methods: The workshop included a lecture by the attending physicist and physician, and hands-on image registration practice by learners (medical physics residents, MP; and radiation oncology residents, RO). All participants filled out pre- and postactivity surveys and rated their comfort from 1 to 10 in (A) selecting what type of imaging to order for a given case and (B) independently assessing the setup quality based on imaging. A paired 1-tailed t test (α = 0.05) was used to evaluate significance; Spearman rank correlation coefficient was used to assess correlation of ratings and RO postgraduate year (PGY). Surveys had free-response questions about IPE and image verification activities in residency.Results: A total of 71 residents from 7 institutions participated between 2018 and 2020. Pre- and postsurveys were completed by 50 residents (38RO, 12MP) and showed an increase in (A) from 5.5 ± 2.2 to 7.1 ± 1.6 (P < .001) and in (B) from 5.1 ± 2.3 to 6.8 ± 1.5 (P < .001), with significant increases per subgroup (AΔ, RO = 1.8 ± 1.7, P < .001; BΔ, RO = 1.9 ± 1.8, P <. 001; AΔ, MP = 1.1 ± 1.4, P = .012; BΔ, MP = 1.2 ± 1.6, P = .016). RO confidence scores moderately correlated with PGY. Survey responses indicated that image verification training is mostly unstructured, with extent of exposure varying by program and attending; most with little-to-no training. Time constraints were identified as the main barrier. IPE was noted as a useful way to incorporate different perspectives into the process.Conclusions: Formal image verification training increases resident comfort with setup imaging review and provides opportunities for interprofessional collaboration in radiation oncology residency programs. [ABSTRACT FROM AUTHOR]

We present a system to hide a full color image inside another of the same size with minimal quality loss to either image. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources. Beyond demonstrating the successful application of deep learning to hiding images, we examine how the result is achieved and apply numerous transformations to analyze if image quality in the host and hidden image can be maintained. These transformation range from simple image manipulations to sophisticated machine learning-based adversaries. Two extensions to the basic system are presented that mitigate the possibility of discovering the content of the hidden image. With these extensions, not only can the hidden information be kept secure, but the system can be used to hide even more than a single image. Applications for this technology include image authentication, digital watermarks, finding exact regions of image manipulation, and storing meta-information about image rendering and content. [ABSTRACT FROM AUTHOR]