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

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

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

Media Forensics and DeepFakes: an overview

With the rapid progress of recent years, techniques that generate and manipulate multimedia content can now guarantee a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos. So-called deepfakes can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Potential abuses are limited only by human imagination. Therefore, there is an urgent need for automated tools capable of detecting false multimedia content and avoiding the spread of dangerous false information. This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos. Special emphasis will be placed on the emerging phenomenon of deepfakes and, from the point of view of the forensic analyst, on modern data-driven forensic methods. The analysis will help to highlight the limits of current forensic tools, the most relevant issues, the upcoming challenges, and suggest future directions for research

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

Photo response non-uniformity based image forensics in the presence of challenging factors

With the ever-increasing prevalence of digital imaging devices and the rapid development of networks, the sharing of digital images becomes ubiquitous in our daily life. However, the pervasiveness of powerful image-editing tools also makes the digital images an easy target for malicious manipulations. Thus, to prevent people from falling victims to fake information and trace the criminal activities, digital image forensics methods like source camera identification, source oriented image clustering and image forgery detections have been developed. Photo response non-uniformity (PRNU), which is an intrinsic sensor noise arises due to the pixels non-uniform response to the incident, has been used as a powerful tool for image device fingerprinting. The forensic community has developed a vast number of PRNU-based methods in different fields of digital image forensics. However, with the technology advancement in digital photography, the emergence of photo-sharing social networking sites, as well as the anti-forensics attacks targeting the PRNU, it brings new challenges to PRNU-based image forensics. For example, the performance of the existing forensic methods may deteriorate due to different camera exposure parameter settings and the efficacy of the PRNU-based methods can be directly challenged by image editing tools from social network sites or anti-forensics attacks. The objective of this thesis is to investigate and design effective methods to mitigate some of these challenges on PRNU-based image forensics. We found that the camera exposure parameter settings, especially the camera sensitivity, which is commonly known by the name of the ISO speed, can influence the PRNU-based image forgery detection. Hence, we first construct the Warwick Image Forensics Dataset, which contains images taken with diverse exposure parameter settings to facilitate further studies. To address the impact from ISO speed on PRNU-based image forgery detection, an ISO speed-specific correlation prediction process is proposed with a content-based ISO speed inference method to facilitate the process even if the ISO speed information is not available. We also propose a three-step framework to allow the PRNUbased source oriented clustering methods to perform successfully on Instagram images, despite some built-in image filters from Instagram may significantly distort PRNU. Additionally, for the binary classification of detecting whether an image's PRNU is attacked or not, we propose a generative adversarial network-based training strategy for a neural network-based classifier, which makes the classifier generalize better for images subject to unprecedented attacks. The proposed methods are evaluated on public benchmarking datasets and our Warwick Image Forensics Dataset, which is released to the public as well. The experimental results validate the effectiveness of the methods proposed in this thesis