Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Forensic Research on Solid State Drives using Trim Analysis

There has been a tremendous change in the way we store data for the past decade. Hard Disk Drives, which were one of the major sources of storing data, are being replaced with Solid State Drives considering the higher efficiency and portability. Digital forensics has been very successful in recovering data from Hard Disk Drives in the past couple of years and has been very well established with Hard Disk Drives. The evolution of Solid State Drives over Hard Drive Drives is posing a lot of challenges to digital forensics as there are many crucial factors to be considering the architecture and the way data is stored in Solid State Drives. This paper gives a very detailed picture of the evolution of Solid State Drives over Hard Disk Drives. We understand the differences in their architecture and the ways to extract data from them. We further discuss in detail the various challenges Solid State Drives pose to the field of digital forensics, and we try to answer contradictory beliefs those are 1) Would data be permanently deleted in a Solid State Drives destroying the forensic evidence required to solve a case? 2) Can data be restored in a Solid State Drives by using proper techniques and still can be used as evidence in digital forensics? In this paper, we talk about the introduction of concepts such as the TRIM Command and Garbage collection, their importance, and we set up an experimental scenario where we implement the TRIM command and try extracting data from different types of Solid State Drives. We compare and evaluate the results obtained through the experiment and try to analyze the uses of the TRIM command and its performance over various Solid State Drives. The paper also discusses future work to make the role of Solid State Drives more efficient in digital forensics

Chinese Expert Consensus on Critical Care Ultrasound Applications at COVID-19 Pandemic

The spread of new coronavirus (SARS-Cov-2) follows a different pattern than previous respiratory viruses, posing a serious public health risk worldwide. World Health Organization (WHO) named the disease as COVID-19 and declared it a pandemic. COVID-19 is characterized by highly contagious nature, rapid transmission, swift clinical course, profound worldwide impact, and high mortality among critically ill patients. Chest X-ray, computerized tomography (CT), and ultrasound are commonly used imaging modalities. Among them, ultrasound, due to its portability and non-invasiveness, can be easily moved to the bedside for examination at any time. In addition, with use of 4G or 5G networks, remote ultrasound consultation can also be performed, which allows ultrasound to be used in isolated medial areas. Besides, the contact surface of ultrasound probe with patients is small and easy to be disinfected. Therefore, ultrasound has gotten lots of positive feedbacks from the frontline healthcare workers, and it has played an indispensable role in the course of COVID-19 diagnosis and follow up

Feature Selection on Permissions, Intents and APIs for Android Malware Detection

Malicious applications pose an enormous security threat to mobile computing devices. Currently 85% of all smartphones run Android, Google’s open-source operating system, making that platform the primary threat vector for malware attacks. Android is a platform that hosts roughly 99% of known malware to date, and is the focus of most research efforts in mobile malware detection due to its open source nature. One of the main tools used in this effort is supervised machine learning. While a decade of work has made a lot of progress in detection accuracy, there is an obstacle that each stream of research is forced to overcome, feature selection, i.e., determining which attributes of Android are most effective as inputs into machine learning models. This dissertation aims to address that problem by providing the community with an exhaustive analysis of the three primary types of Android features used by researchers: Permissions, Intents and API Calls. The intent of the report is not to describe a best performing feature set or a best performing machine learning model, nor to explain why certain Permissions, Intents or API Calls get selected above others, but rather to provide a holistic methodology to help guide feature selection for Android malware detection. The experiments used eleven different feature selection techniques covering filter methods, wrapper methods and embedded methods. Each feature selection technique was applied to seven different datasets based on the seven combinations available of Permissions, Intents and API Calls. Each of those seven datasets are from a base set of 119k Android apps. All of the result sets were then validated against three different machine learning models, Random Forest, SVM and a Neural Net, to test applicability across algorithm type. The experiments show that using a combination of Permissions, Intents and API Calls produced higher accuracy than using any of those alone or in any other combination and that feature selection should be performed on the combined dataset, not by feature type and then combined. The data also shows that, in general, a feature set size of 200 or more attributes is required for optimal results. Finally, the feature selection methods Relief, Correlation-based Feature Selection (CFS) and Recursive Feature Elimination (RFE) using a Neural Net are not satisfactory approaches for Android malware detection work. Based on the proposed methodology and experiments, this research provided insights into feature selection – a significant but often overlooked issue in Android malware detection. We believe the results reported herein is an important step for effective feature evaluation and selection in assisting malware detection especially for datasets with a large number of features. The methodology also has the potential to be applied to similar malware detection tasks or even in broader domains such as pattern recognition

Clinical, Biomechanical, and Physiological Translational Interpretations of Human Resting Myofascial Tone or Tension

Background: Myofascial tissues generate integrated webs and networks of passive and active tensional forces that provide stabilizing support and that control movement in the body. Passive [central nervous system (CNS)–independent] resting myofascial tension is present in the body and provides a low-level stabilizing component to help maintain balanced postures. This property was recently called “human resting myofascial tone” (HRMT). The HRMT model evolved from electromyography (EMG) research in the 1950s that showed lumbar muscles usually to be EMG-silent in relaxed gravity-neutral upright postures. Methods: Biomechanical, clinical, and physiological studies were reviewed to interpret the passive stiffness properties of HRMT that help to stabilize various relaxed functions such as quiet balanced standing. Biomechanical analyses and experimental studies of the lumbar multifidus were reviewed to interpret its passive stiffness properties. The lumbar multifidus was illustrated as the major core stabilizing muscle of the spine, serving an important passive biomechanical role in the body. Results: Research into muscle physiology suggests that passive resting tension (CNS-independent) is generated in sarcomeres by the molecular elasticity of low-level cycling cross-bridges between the actomyosin filaments. In turn, tension is complexly transmitted to intimately enveloping fascial matrix fibrils and other molecular elements in connective tissue, which, collectively, constitute the myofascial unit. Postural myofascial tonus varies with age and sex. Also, individuals in the population are proposed to vary in a polymorphism of postural HRMT. A few people are expected to have outlier degrees of innate postural hypotonicity or hypertonicity. Such biomechanical variations likely predispose to greater risk of related musculoskeletal disorders, a situation that deserves greater attention in clinical practice and research. Axial myofascial hypertonicity was hypothesized to predispose to ankylosing spondylitis. This often-progressive deforming condition of vertebrae and sacroiliac joints is characterized by stiffness features and particular localization of bony lesions at entheseal sites. Such unique features imply concentrations and transmissions of excessive force, leading to tissue micro-injury and maladaptive repair reactions. Conclusions: The HRMT model is now expanded and translated for clinical relevance to therapists. Its passive role in helping to maintain balanced postures is supported by biomechanical principles of myofascial elasticity, tension, stress, stiffness, and tensegrity. Further research is needed to determine the molecular basis of HRMT in sarcomeres, the transmission of tension by the enveloping fascial elements, and the means by which the myofascia helps to maintain efficient passive postural balance in the body. Significant deficiencies or excesses of postural HRMT may predispose to symptomatic or pathologic musculoskeletal disorders whose mechanisms are currently unexplained

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it