Source identification in image forensics

Source identification is one of the most important tasks in digital image forensics. In fact, the ability to reliably associate an image with its acquisition device may be crucial both during investigations and before a court of law. For example, one may be interested in proving that a certain photo was taken by his/her camera, in order to claim intellectual property. On the contrary, it may be law enforcement agencies that are interested to trace back the origin of some images, because they violate the law themselves (e.g. do not respect privacy laws), or maybe they point to subjects involved in unlawful and dangerous activities (like terrorism, pedo-pornography, etc). More in general, proving, beyond reasonable doubts, that a photo was taken by a given camera, may be an important element for decisions in court. The key assumption of forensic source identification is that acquisition devices leave traces in the acquired content, and that instances of these traces are specific to the respective (class of) device(s). This kind of traces is present in the so-called device fingerprint. The name stems from the forensic value of human fingerprints. Motivated by the importance of the source identification in digital image forensics community and the need of reliable techniques using device fingerprint, the work developed in the Ph.D. thesis concerns different source identification level, using both feature-based and PRNU-based approach for model and device identification. In addition, it is also shown that counter-forensics methods can easily attack machine learning techniques for image forgery detection. In model identification, an analysis of hand-crafted local features and deep learning ones has been considered for the basic two-class classification problem. In addition, a comparison with the limited knowledge and the blind scenario are presented. Finally, an application of camera model identification on various iris sensor models is conducted. A blind scenario technique that faces the problem of device source identification using the PRNU-based approach is also proposed. With the use of the correlation between single-image sensor noise, a blind two-step source clustering is proposed. In the first step correlation clustering together with ensemble method is used to obtain an initial partition, which is then refined in the second step by means of a Bayesian approach. Experimental results show that this proposal outperforms the state-of-the-art techniques and still give an acceptable performance when considering images downloaded from Facebook

CNN-based fast source device identification

Source identification is an important topic in image forensics, since it allows to trace back the origin of an image. This represents a precious information to claim intellectual property but also to reveal the authors of illicit materials. In this paper we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks (CNNs). Specifically, we propose a 2-channel-based CNN that learns a way of comparing camera fingerprint and image noise at patch level. The proposed solution turns out to be much faster than the conventional approach and to ensure an increased accuracy. This makes the approach particularly suitable in scenarios where large databases of images are analyzed, like over social networks. In this vein, since images uploaded on social media usually undergo at least two compression stages, we include investigations on double JPEG compressed images, always reporting higher accuracy than standard 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

Improved Tampering Localization in Digital image Forensics: Comparative Study Based on Maximal Entropy Random Walk and Multi-Scale Fusion

Nowadays the increasing ease of editing digital photographs has spawned an urgent need for reliable authentication mechanism capable of precise localization of potential malicious forgeries. In this paper we compare two different Techniques to analyze which technique can be used more efficiently in localization of Tampered Region In Digital Image .First Technique is Maximal Entropy Random Walk in which Strong localization property of this random walk will highlight important regions and to diminish the background- even for noisy response maps. Our evaluation will show that the proposed method can significantly perform both the commonly used threshold-based decision, and the recently proposed optimization approach with a Markovian prior. The second Technique which is based on Multi-Scale Fusion will investigate a multi-scale analysis approach which merge multiple candidate tampering maps, obtained from the analysis with different windows, to obtain a single, more efficient tampering map with better localization resolution. We propose three different techniques for multi- scale fusion, and verify their feasibility .In this slant we consider popular tampering scenario to distinguish between singly and doubly compressed region