A Practitioner Survey Exploring the Value of Forensic Tools, AI, Filtering, & Safer Presentation for Investigating Child Sexual Abuse Material

For those investigating cases of Child Sexual Abuse Material (CSAM), there is the potential harm of experiencing trauma after illicit content exposure over a period of time. Research has shown that those working on such cases can experience psychological distress. As a result, there has been a greater effort to create and implement technologies that reduce exposure to CSAM. However, not much work has explored gathering insight regarding the functionality, effectiveness, accuracy, and importance of digital forensic tools and data science technologies from practitioners who use them. This study focused specifically on examining the value practitioners give to the tools and technologies they utilize to investigate CSAM cases. General findings indicated that implementing filtering technologies is more important than safe-viewing technologies; false positives are a greater concern than false negatives; resources such as time, personnel, and money continue to be a concern; and an improved workflow is highly desirable. Results also showed that practitioners are not well-versed in data science and Artificial Intelligence (AI), which is alarming given that tools already implement these techniques and that practitioners face large amounts of data during investigations. Finally, the data exemplified that practitioners are generally not taking advantage of tools that implement data science techniques, and that the biggest need for them is in automated child nudity detection, age estimation and skin tone detection

Context-aware person identification in personal photo collections

Identifying the people in photos is an important need for users of photo management systems. We present MediAssist, one such system which facilitates browsing, searching and semi-automatic annotation of personal photos, using analysis of both image content and the context in which the photo is captured. This semi-automatic annotation includes annotation of the identity of people in photos. In this paper, we focus on such person annotation, and propose person identification techniques based on a combination of context and content. We propose language modelling and nearest neighbor approaches to context-based person identification, in addition to novel face color and image color content-based features (used alongside face recognition and body patch features). We conduct a comprehensive empirical study of these techniques using the real private photo collections of a number of users, and show that combining context- and content-based analysis improves performance over content or context alone

Designing Light Filters to Detect Skin Using a Low-powered Sensor

Detection of nudity in photos and videos, especially prior to uploading to the internet, is vital to solving many problems related to adolescent sexting, the distribution of child pornography, and cyber-bullying. The problem with using nudity detection algorithms as a means to combat these problems is that: 1) it implies that a digitized nude photo of a minor already exists (i.e., child pornography), and 2) there are real ethical and legal concerns around the distribution and processing of child pornography. Once a camera captures an image, that image is no longer secure. Therefore, we need to develop new privacy-preserving solutions that prevent the digital capture of nude imagery of minors. My research takes a first step in trying to accomplish this long-term goal: In this thesis, I examine the feasibility of using a low-powered sensor to detect skin dominance (defined as an image comprised of 50% or more of human skin tone) in a visual scene. By designing four custom light filters to enhance the digital information extracted from 300 scenes captured with the sensor (without digitizing high-fidelity visual features), I was able to accurately detect a skin dominant scene with 83.7% accuracy, 83% precision, and 85% recall. The long-term goal to be achieved in the future is to design a low-powered vision sensor that can be mounted on a digital camera lens on a teen\u27s mobile device to detect and/or prevent the capture of nude imagery. Thus, I discuss the limitations of this work toward this larger goal, as well as future research directions

WEBCAM MOTION DETECTION USINGVISUAL BASIC

Watch Eyes Motion Detection System is aproject used to enhance student's interest in multimedia system development, but at the same time the project can be used as motion detection system that will help any person in doing research in motion detection purposes. This system is able to be upgraded to be amonitoring system for security purposes. The project adds the number of motion detection collection programs that available in the market. Motion detection helps a lot in daily activities that involve security and good monitoring system. Studies have shown that motion detection helps in improvement of the security system and especially in doing aresearch about motion. But it is not the main focus in this project because it focuses more on how motion detection works. Watch Eyes is developed using skin tones algqrithm which is use in the system to detect the skin tones that present in the captured image. The main focus of the system is to ensure that the motion can be detected only using the skin tones. Combination of several related algorithms helps in developing the skin tones algorithm. Many researches have been carried out to identify the needs ofthe system to make it works as expected. The development of this system dope in stages, determined by the planning processes which follow the Water-Spiral model. Early analysis of the existing motion detection system falls under the earlier stage which include information gathering. Through the development of such system, it is hoped that it will help to increase student interest in computer vision that involve multimedia development and as well as to individual who interest in doing motion detection research

Cardiovascular assessment by imaging photoplethysmography – a review

AbstractOver the last few years, the contactless acquisition of cardiovascular parameters using cameras has gained immense attention. The technique provides an optical means to acquire cardiovascular information in a very convenient way. This review provides an overview on the technique’s background and current realizations. Besides giving detailed information on the most widespread application of the technique, namely the contactless acquisition of heart rate, we outline further concepts and we critically discuss the current state.</jats:p

Engineering data compendium. Human perception and performance. User's guide

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use

Validity of resting heart rate derived from contact-based smartphone photoplethysmography compared with electrocardiography:a scoping review and checklist for optimal acquisition and reporting

Background: With the rise of smartphone ownership and increasing evidence to support the suitability of smartphone usage in healthcare, the light source and smartphone camera could be utilized to perform photoplethysmography (PPG) for the assessment of vital signs, such as heart rate (HR). However, until rigorous validity assessment has been conducted, PPG will have limited use in clinical settings.Objective: We aimed to conduct a scoping review assessing the validity of resting heart rate (RHR) acquisition from PPG utilizing contact-based smartphone devices. Our four specific objectives of this scoping review were to (1) conduct a systematic search of the published literature concerning contact-based smartphone device-derived PPG, (2) map study characteristics and methodologies, (3) identify if methodological and technological advancements have been made, and (4) provide recommendations for the advancement of the investigative area.Methods: ScienceDirect, PubMed and SPORTDiscus were searched for relevant studies between January 1st, 2007, and November 6th, 2022. Filters were applied to ensure only literature written in English were included. Reference lists of included studies were manually searched for additional eligible studies.Results: In total 10 articles were included. Articles varied in terms of methodology including study characteristics, index measurement characteristics, criterion measurement characteristics, and experimental procedure. Additionally, there were variations in reporting details including primary outcome measure and measure of validity. However, all studies reached the same conclusion, with agreement ranging between good to very strong and correlations ranging from r = .98 to 1.Conclusions: Smartphone applications measuring RHR derived from contact-based smartphone PPG appear to agree with gold standard electrocardiography (ECG) in healthy subjects. However, agreement was established under highly controlled conditions. Future research could investigate their validity and consider effective approaches that transfer these methods from laboratory conditions into the “real-world”, in both healthy and clinical populations

Nondestructive detection and characterization of damages in honeycomb composite structures

This thesis discusses many existing methods of nondestructive evaluation used on honeycomb structures ranging from ultrasonic transduction to various low frequency techniques. The focus, however, is given to a newly developed technique based on hysteresis effects in force-displacement curves. The area enclosed by the hysteresis loop represents the amount of energy absorbed by the sample during the loading and unloading phases. It is believed that the cause of the energy absorption is due to increased internal frictional loses which occur when the sample is damaged. The loop area was found to correlate well with the level of damage sustained by the structure. This technique is centered on the deduction of a force-displacement curve from an accelerometer tap, which allows the force-displacement curves to be generated from a single tap on one surface of the structure. Traditionally a force-displacement curve, the equivalent of a stress-strain curve, is produced using a mechanical testing machine. However, this is not a suitable method to be used to attain a force-displacement curve while a structure is in-service because it requires access to both the front and back surfaces of the structure. The deduction of a force-displacement curve from an accelerometer tap proved to be an effect solution to this problem. The great advantage of this method is that it only requires access to one surface of the structure to generate a force-displacement curve. This method also takes much less time to generate the hysteresis loops. A mechanical testing machine could take up to 15 minutes to produce a single force-displacement curve, this method will produce the same curve in seconds. Much of this research was devoted to the testing and development of the techniques used to deduce a force-displacement curve from an accelerometer tap. This thesis also takes a look at the benefits of two-dimensional Fourier Transforms. During the course of this research, many C-scans of honeycomb composite structures were generated using air-couple ultrasonics. These C-scans were used as baseline images to compare with the results of the newly developed mechanical hysteresis technique. The honeycomb structure can cause very distracting hexagonal patterns in ultrasonic images. The Fourier transform and the processing associated with it is able to suppress these distracting patterns while leaving the rest of the image relatively unaffected. In some cases, not only can the patterns be suppressed, but the flaws can also be drawn out. A program was written to perform and filter the two-dimensional Fourier Transforms to suppress the patterns from the C-scan images

Melanoma classification using deep transfer learning

Melanoma is the most lethal type of skin cancer, despite the fact that individuals who are discovered early have a decent chance of recovering. A few creators have looked at various strategies to deal with programmed location and conclusion using design recognition and AI technology. Anticipating an infection so that it does not spread It is often helpful when doctors can diagnose an illness early on and spread throughout the body. Early disease detection is quite difficult due to the small number of screening populations. Whatever the case, it will take time to determine if it is harmless or hazardous. Assume the afflicted person sees a critical specialist for analysis, unaware that the critical specialist's knowledge has resulted in a cancerous development. This is where AI and deep learning technologies become a vital component of an effective mechanised determination framework, which might help doctors forecast infections much more swiftly and even ordinary people analyse a sickness. Our study endeavour addresses the issues of increased clinical expenditures associated with discovery, lower Precision in recognition and the manual discovery framework's mobility. System for Detecting Malignant Growths in Melanoma is a deep learning-based predictive model that leverages thermoscope pictures

Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection

Image splicing detection is an area of studies that have been studied widely all around the world recently. The importance to do image splicing detection is not only for the authorities but also for common user. Image splicing detection requires several steps to be completed and a huge dataset is needed to be used. This study is aimed to investigate and develop CNN based method for image splicing detection. Three preliminary experiments are done according to previous work to observe how pre-processing affects CNN performance. Based on the preliminary experiments, an architecture with reduced number of CNN layers are proposed without any pre-processing. Ten-fold cross validation is used to demonstrate CNN performance. Preliminary experiments shows that CNN performance are critically affected by input image size. Therefore, the proposed architecture are tested with different input image sizes. Three different input image sizes are tested which are 28×28 pixel, 64×64 pixel and 128×128 pixels. From cross validation is can be concluded that 64×64 pixels input image is the most suitable input image size for CNN image splicing detection. At the end of this study, it is observed that by using the proposed architecture, CNN can be used for image splicing detection without any pre-processing