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

Source Camera Device Identification from Videos

Source camera identification is an important and challenging problem in digital image forensics. The clues of the device used to capture the digital media are very useful for Law Enforcement Agencies (LEAs), especially to help them collect more intelligence in digital forensics. In our work, we focus on identifying the source camera device based on digital videos using deep learning methods. In particular, we evaluate deep learning models with increasing levels of complexity for source camera identification and show that with such sophistication the scene-suppression techniques do not aid in model performance. In addition, we mention several common machine learning strategies that are counter-productive in achieving a high accuracy for camera identification. We conduct systematic experiments using 28 devices from the VISION data set and evaluate the model performance on various video scenarios—flat (i.e., homogeneous), indoor, and outdoor and evaluate the impact on classification accuracy when the videos are shared via social media platforms such as YouTube and WhatsApp. Unlike traditional PRNU-noise (Photo Response Non-Uniform)-based methods which require flat frames to estimate camera reference pattern noise, the proposed method has no such constraint and we achieve an accuracy of $$72.75 \pm 1.1 \%$$on the benchmark VISION data set. Furthermore, we also achieve state-of-the-art accuracy of $$71.75\%$$on the QUFVD data set in identifying 20 camera devices. These two results are the best ever reported on the VISION and QUFVD data sets. Finally, we demonstrate the runtime efficiency of the proposed approach and its advantages to LEAs

Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces

Recent advances in deep learning have enabled forensics researchers to develop a new class of image splicing detection and localization algorithms. These algorithms identify spliced content by detecting localized inconsistencies in forensic traces using Siamese neural networks, either explicitly during analysis or implicitly during training. At the same time, deep learning has enabled new forms of anti-forensic attacks, such as adversarial examples and generative adversarial network (GAN) based attacks. Thus far, however, no anti-forensic attack has been demonstrated against image splicing detection and localization algorithms. In this paper, we propose a new GAN-based anti-forensic attack that is able to fool state-of-the-art splicing detection and localization algorithms such as EXIF-Net, Noiseprint, and Forensic Similarity Graphs. This attack operates by adversarially training an anti-forensic generator against a set of Siamese neural networks so that it is able to create synthetic forensic traces. Under analysis, these synthetic traces appear authentic and are self-consistent throughout an image. Through a series of experiments, we demonstrate that our attack is capable of fooling forensic splicing detection and localization algorithms without introducing visually detectable artifacts into an attacked image. Additionally, we demonstrate that our attack outperforms existing alternative attack approaches.

A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing. This is not a problem for high-level vision problems, where discriminative features are barely affected by resizing. On the contrary, in image forensics, resizing tends to destroy precious high-frequency details, impacting heavily on performance. One can avoid resizing by means of patch-wise processing, at the cost of renouncing whole-image analysis. In this work, we propose a CNN-based image forgery detection framework which makes decisions based on full-resolution information gathered from the whole image. Thanks to gradient checkpointing, the framework is trainable end-to-end with limited memory resources and weak (image-level) supervision, allowing for the joint optimization of all parameters. Experiments on widespread image forensics datasets prove the good performance of the proposed approach, which largely outperforms all baselines and all reference methods.Comment: 13 pages, 12 figures, journa

A review of digital video tampering: from simple editing to full synthesis.

Video tampering methods have witnessed considerable progress in recent years. This is partly due to the rapid development of advanced deep learning methods, and also due to the large volume of video footage that is now in the public domain. Historically, convincing video tampering has been too labour intensive to achieve on a large scale. However, recent developments in deep learning-based methods have made it possible not only to produce convincing forged video but also to fully synthesize video content. Such advancements provide new means to improve visual content itself, but at the same time, they raise new challenges for state-of-the-art tampering detection methods. Video tampering detection has been an active field of research for some time, with periodic reviews of the subject. However, little attention has been paid to video tampering techniques themselves. This paper provides an objective and in-depth examination of current techniques related to digital video manipulation. We thoroughly examine their development, and show how current evaluation techniques provide opportunities for the advancement of video tampering detection. A critical and extensive review of photo-realistic video synthesis is provided with emphasis on deep learning-based methods. Existing tampered video datasets are also qualitatively reviewed and critically discussed. Finally, conclusions are drawn upon an exhaustive and thorough review of tampering methods with discussions of future research directions aimed at improving detection methods

Camera model identification based on forensic traces extracted from homogeneous patches

A crucial challenge in digital image forensics is to identify the source camera model used to generate given images. This is of prime importance, especially for Law Enforcement Agencies in their investigations of Child Sexual Abuse Material found in darknets or seized storage devices. In this work, we address this challenge by proposing a solution that is characterized by two main contributions. It relies on the extraction of rather small homogeneous regions that we extract very efficiently from the integral image, and on a hierarchical classification approach with convolutional neural networks as the underlying models. We rely on homogeneous regions as they contain camera traces that are less distorted than regions with high-level scene content. The hierarchical approach that we propose is important for scaling up and making minimal modifications when new cameras are added. Furthermore, this scheme performs better than the traditional single classifier approach. By means of thorough experimentation on the publicly available Dresden data set, we achieve an accuracy of 99.01% with 5-fold cross-validation on the ‘natural’ subset of this data set. To the best of our knowledge, this is the best result ever reported for Dresden data set