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

The Design of a Multimedia-Forensic Analysis Tool (M-FAT)

Digital forensics has become a fundamental requirement for law enforcement due to the growing volume of cyber and computer-assisted crime. Whilst existing commercial tools have traditionally focused upon string-based analyses (e.g., regular expressions, keywords), less effort has been placed towards the development of multimedia-based analyses. Within the research community, more focus has been attributed to the analysis of multimedia content; they tend to focus upon highly specialised specific scenarios such as tattoo identification, number plate recognition, suspect face recognition and manual annotation of images. Given the everincreasing volume of multimedia content, it is essential that a holistic Multimedia-Forensic Analysis Tool (M-FAT) is developed to extract, index, analyse the recovered images and provide an investigator with an environment with which to ask more abstract and cognitively challenging questions of the data. This paper proposes such a system, focusing upon a combination of object and facial recognition to provide a robust system. This system will enable investigators to perform a variety of forensic analyses that aid in reducing the time, effort and cognitive load being placed on the investigator to identify relevant evidence

Detection of human face from sketches using deep learning networks

In the law and enforcement, sketching is a common technique to record witnesses’ version of perceived suspects. Then these sketches are employed to identify the correct persons from a photo database, which will be a time consuming task if not impossible for a human being. This work investigates the application of artificial techniques (AI), specifically, the recent state of the art deep learning techniques to identify people based on their sketches for law and enforcement. The dataset is collected from Chinese database (CUHK student dataset) that includes both photos and their corresponding sketches. The sketches are drawn by artists. The deep learning network is Siamese network, which uses Python programming language. Since the data base contains only less than 200 photos, data enlargement is also conducted to generate sketch images from face images, which generates another 500 pairs of images. As a result, the Siamese network achieved 96% accuracy when retrieving photos from sketch images. Future work include further enlarging the database and evaluating other deep learning architectures

Delving Deep into the Sketch and Photo Relation

"Sketches drawn by humans can play a similar role to photos in terms of conveying shape, posture as well as fine-grained information, and this fact has stimulated one line of cross-domain research that is related to sketch and photo, including sketch-based photo synthesis and retrieval. In this thesis, we aim to further investigate the relationship between sketch and photo. More specifically, we study certain under- explored traits in this relationship, and propose novel applications to reinforce the understanding of sketch and photo relation.Our exploration starts with the problem of sketch-based photo synthesis, where the unique trait of non-rigid alignment between sketch and photo is overlooked in existing research. We then carry on with our investigation from a new angle to study whether sketch can facilitate photo classifier generation. Building upon this, we continue to explore how sketch and photo are linked together on a more fine-grained level by tackling with the sketch-based photo segmenter prediction. Furthermore, we address the data scarcity issue identified in nearly all sketch-photo-related applications by examining their inherent correlation in the semantic aspect using sketch-based image retrieval (SBIR) as a test-bed. In general, we make four main contributions to the research on relationship between sketch and photo.Firstly, to mitigate the effect of deformation in sketch-based photo synthesis, we introduce the spatial transformer network to our image-image regression framework, which subtly deals with non-rigid alignment between the sketches and photos. The qualitative and quantitative experiments consistently reveal the superior quality of our synthesised photos over those generated by existing approaches.Secondly, sketch-based photo classifier generation is achieved with a novel model regression network, which maps the sketch to the parameters of photo classification model. It is shown that our model regression network is able to generalise across categories and photo classifiers for novel classes not involved in training are just a sketch away. Comprehensive experiments illustrate the promising performance of the generated binary and multi-class photo classifiers, and demonstrate that sketches can also be employed to enhance the granularity of existing photo classifiers.Thirdly, to achieve the goal of sketch-based photo segmentation, we propose a photo segmentation model generation algorithm that predicts the weights of a deep photo segmentation network according to the input sketch. The results confirm that one single sketch is the only prerequisite for unseen category photo segmentation, and the segmentation performance can be further improved by utilising sketch that is aligned with the object to be segmented in shape and position.Finally, we present an unsupervised representation learning framework for SBIR, the purpose of which is to eliminate the barrier imposed by data annotation scarcity. Prototype and memory bank reinforced joint distribution optimal transport is integrated into the unsupervised representation learning framework, so that the mapping between the sketches and photos could be automatically detected to learn a semantically meaningful yet domain-agnostic feature space. Extensive experiments and feature visualisation validate the efficacy of our proposed algorithm.

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

Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review

The task of multimedia geolocation is becoming an increasingly essential component of the digital forensics toolkit to effectively combat human trafficking, child sexual exploitation, and other illegal acts. Typically, metadata-based geolocation information is stripped when multimedia content is shared via instant messaging and social media. The intricacy of geolocating, geotagging, or finding geographical clues in this content is often overly burdensome for investigators. Recent research has shown that contemporary advancements in artificial intelligence, specifically computer vision and deep learning, show significant promise towards expediting the multimedia geolocation task. This systematic literature review thoroughly examines the state-of-the-art leveraging computer vision techniques for multimedia geolocation and assesses their potential to expedite human trafficking investigation. This includes a comprehensive overview of the application of computer vision-based approaches to multimedia geolocation, identifies their applicability in combating human trafficking, and highlights the potential implications of enhanced multimedia geolocation for prosecuting human trafficking. 123 articles inform this systematic literature review. The findings suggest numerous potential paths for future impactful research on the subject

Deep Learning for Free-Hand Sketch: A Survey

Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.Comment: This paper is accepted by IEEE TPAM