Clustering techniques applied to forensic video analysis

As number of mobile devices arise, and most of them equipped with a camera, it becomes not only easier to generate video content, but harder to identify or classify the source of it. On this field many papers had been written, most of them exploring the PNRU noise, with good results, even if it requires a lot of computation. This work proposes an algorithm to cluster a set of videos based only on the information of the container of the video, with lower computational cost than content based approaches

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

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

Video MOV authentication using H.264/AVC container

Trabajo Fin de Máster en Ingeniería Informática, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2019/2020.En la actualidad la cámara fotográfica más popular del mundo es el teléfono móvil. Estos dispositivos traen nuevas funciones que crecen vertiginosamente y hacen que sean de uso general por la mayoría de personas. Todavía quedan cosas por mejorar pero dada la inversión realizada en ellos, es solo cuestión de tiempo para que se hagan con el mercado. Este marco hace que el análisis a nivel forense de videos tome mucho peso y sea necesario y significativo en un sin número de circunstancias como pueden ser pruebas judiciales, pesquisas, abuso de menores, sustitución de identidad, etc. En este trabajo se ha desarrollado un método que analiza el contenido del contenedor de vídeos con formato MOV. La técnica se cimienta en la extracción de información contenida en el contenedor multimedia y operaciones de análisis de datos. El objetivo del método es analizar la información extraída a nivel de dispositivos, modelos y fabricantes de dispositivos móviles para identificar el origen de un vídeo dado. Además, haciendo uso de otra herramienta previamente desarrollada se realiza comparaciones de la información extraída de videos procesados por redes sociales y aplicaciones de edición de vídeos. Las herramientas u origen de información usadas para el progreso de la técnica propuesta son por un lado, las especificaciones de los estándares y por otro, las investigaciones previas dedicadas al análisis de otros tipos de contenedores multimedia. Asimismo, para el análisis se hace uso de un dataset de vídeos con características esenciales para que el resultado sea eficiente y lo más preciso posible.Currently the most popular camera in the world is the mobile phone. these devices bring new functions that grow rapidly and make them commonly used by most people. there are still things to improve but given the investment made in them, it is only a matter of time for them to take over the market. this framework makes the forensic analysis of videos take a lot of weight and is necessary and signifcant in countless circumstances such as judicial evidence, investigations, child abuse, identity substitution, etc. this work has developed a method that analyzes the content of the container of videos in MOV format. the technique is based on the extraction of information contained in the multimedia container and data analysis operations. the objective of the method is to analyze the information extracted at the level of devices, models and manufacturers of mobile devices to identify the origin of a given video. In addition, using another previously developed tool, comparisons are made of the information extracted from videos processed by social networks and video editing applications. the tools or source of information used for the progress of the proposed technique are, on the one hand, the specifcations of the standards and, on the other, the previous investigations dedicated to the analysis of other types of multimedia containers. Also, the analysis uses a video dataset with essential characteristics so that the result is effcient and as accurate as possible.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

A semantic methodology for (un)structured digital evidences analysis

Nowadays, more than ever, digital forensics activities are involved in any criminal, civil or military investigation and represent a fundamental tool to support cyber-security. Investigators use a variety of techniques and proprietary software forensic applications to examine the copy of digital devices, searching hidden, deleted, encrypted, or damaged files or folders. Any evidence found is carefully analysed and documented in a "finding report" in preparation for legal proceedings that involve discovery, depositions, or actual litigation. The aim is to discover and analyse patterns of fraudulent activities. In this work, a new methodology is proposed to support investigators during the analysis process, correlating evidences found through different forensic tools. The methodology was implemented through a system able to add semantic assertion to data generated by forensics tools during extraction processes. These assertions enable more effective access to relevant information and enhanced retrieval and reasoning capabilities

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