Exploring Biomedical Video Source Identification: Transitioning from Fuzzy-Based Systems to Machine Learning Models

In recent years, the field of biomedical video source identification has witnessed a significant evolution driven by advances in both fuzzy-based systems and machine learning models. This paper presents a comprehensive survey of the current state of the art in this domain, highlighting the transition from traditional fuzzy-based approaches to the emerging dominance of machine learning techniques. Biomedical videos have become integral in various aspects of healthcare, from medical imaging and diagnostics to surgical procedures and patient monitoring. The accurate identification of the sources of these videos is of paramount importance for quality control, accountability, and ensuring the integrity of medical data. In this context, source identification plays a critical role in establishing the authenticity and origin of biomedical videos. This survey delves into the evolution of source identification methods, covering the foundational principles of fuzzy-based systems and their applications in the biomedical context. It explores how linguistic variables and expert knowledge were employed to model video sources, and discusses the strengths and limitations of these early approaches. By surveying existing methodologies and databases, this paper contributes to a broader understanding of the field’s progress and challenges

PRNU-Based Camera Attribution from Multiple Seam-Carved Images

Photo response non-uniformity (PRNU) noise-based source attribution is a well-known technique to verify the camera of an image or video. Researchers have proposed various countermeasures to prevent PRNU-based source camera attribution. Forced seam-carving is one such recently proposed counter forensics technique. This technique can disable PRNU-based source camera attribution by forcefully removing seams such that the size of most uncarved image blocks is less than $50 \times 50$ pixels. In this paper, we show that given multiple seam-carved images from the same camera, source attribution can still be possible even if the size of uncarved blocks in the image is less than the recommended size of $50 \times 50$ pixels. Theoretical analysis and experiments with multiple cameras demonstrate that the effectiveness of our scheme depends on the number of seams carved from an image and the randomness of the seam positions

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

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

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field