Perceptual MAE for Image Manipulation Localization: A High-level Vision Learner Focusing on Low-level Features

Nowadays, multimedia forensics faces unprecedented challenges due to the rapid advancement of multimedia generation technology thereby making Image Manipulation Localization (IML) crucial in the pursuit of truth. The key to IML lies in revealing the artifacts or inconsistencies between the tampered and authentic areas, which are evident under pixel-level features. Consequently, existing studies treat IML as a low-level vision task, focusing on allocating tampered masks by crafting pixel-level features such as image RGB noises, edge signals, or high-frequency features. However, in practice, tampering commonly occurs at the object level, and different classes of objects have varying likelihoods of becoming targets of tampering. Therefore, object semantics are also vital in identifying the tampered areas in addition to pixel-level features. This necessitates IML models to carry out a semantic understanding of the entire image. In this paper, we reformulate the IML task as a high-level vision task that greatly benefits from low-level features. Based on such an interpretation, we propose a method to enhance the Masked Autoencoder (MAE) by incorporating high-resolution inputs and a perceptual loss supervision module, which is termed Perceptual MAE (PMAE). While MAE has demonstrated an impressive understanding of object semantics, PMAE can also compensate for low-level semantics with our proposed enhancements. Evidenced by extensive experiments, this paradigm effectively unites the low-level and high-level features of the IML task and outperforms state-of-the-art tampering localization methods on all five publicly available datasets

ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization

With the spread of tampered images, locating the tampered regions in digital images has drawn increasing attention. The existing image tampering localization methods, however, suffer from severe performance degradation when the tampered images are subjected to some post-processing, as the tampering traces would be distorted by the post-processing operations. The poor robustness against post-processing has become a bottleneck for the practical applications of image tampering localization techniques. In order to address this issue, this paper proposes a novel restoration-assisted framework for image tampering localization (ReLoc). The ReLoc framework mainly consists of an image restoration module and a tampering localization module. The key idea of ReLoc is to use the restoration module to recover a high-quality counterpart of the distorted tampered image, such that the distorted tampering traces can be re-enhanced, facilitating the tampering localization module to identify the tampered regions. To achieve this, the restoration module is optimized not only with the conventional constraints on image visual quality but also with a forensics-oriented objective function. Furthermore, the restoration module and the localization module are trained alternately, which can stabilize the training process and is beneficial for improving the performance. The proposed framework is evaluated by fighting against JPEG compression, the most commonly used post-processing. Extensive experimental results show that ReLoc can significantly improve the robustness against JPEG compression. The restoration module in a well-trained ReLoc model is transferable. Namely, it is still effective when being directly deployed with another tampering localization module.Comment: 12 pages, 5 figure

Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review

With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of benchmark datasets for malicious manipulation detection that are publicly available. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena

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

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