A vision-based fully automated approach to robust image cropping detection

The definition of valid and robust methodologies for assessing the authenticity of digital information is nowadays critical to contrast social manipulation through the media. A key research topic in multimedia forensics is the development of methods for detecting tampered content in large image collections without any human intervention. This paper introduces AMARCORD (Automatic Manhattan-scene AsymmetRically CrOpped imageRy Detector), a fully automated detector for exposing evidences of asymmetrical image cropping on Manhattan-World scenes. The proposed solution estimates and exploits the camera principal point, i.e., a physical feature extracted directly from the image content that is quite insensitive to image processing operations, such as compression and resizing, typical of social media platforms. Robust computer vision techniques are employed throughout, so as to cope with large sources of noise in the data and improve detection performance. The method leverages a novel metric based on robust statistics, and is also capable to decide autonomously whether the image at hand is tractable or not. The results of an extensive experimental evaluation covering several cropping scenarios demonstrate the effectiveness and robustness of our approac

Image Forgery Localization via Block-Grained Analysis of JPEG Artifacts

In this paper, we propose a forensic algorithm to discriminate between original and forged regions in JPEG images, under the hypothesis that the tampered image presents a double JPEG compression, either aligned (A-DJPG) or non-aligned (NA-DJPG). Unlike previous approaches, the proposed algorithm does not need to manually select a suspect region in order to test the presence or the absence of double compression artifacts. Based on an improved and unified statistical model characterizing the artifacts that appear in the presence of both A-DJPG or NA-DJPG, the proposed algorithm automatically computes a likelihood map indicating the probability for each $8 \times 8$ discrete cosine transform block of being doubly compressed. The validity of the proposed approach has been assessed by evaluating the performance of a detector based on thresholding the likelihood map, considering different forensic scenarios. The effectiveness of the proposed method is also confirmed by tests carried on realistic tampered images. An interesting property of the proposed Bayesian approach is that it can be easily extended to work with traces left by other kinds of processin

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

Resiliency Assessment and Enhancement of Intrinsic Fingerprinting

Intrinsic fingerprinting is a class of digital forensic technology that can detect traces left in digital multimedia data in order to reveal data processing history and determine data integrity. Many existing intrinsic fingerprinting schemes have implicitly assumed favorable operating conditions whose validity may become uncertain in reality. In order to establish intrinsic fingerprinting as a credible approach to digital multimedia authentication, it is important to understand and enhance its resiliency under unfavorable scenarios. This dissertation addresses various resiliency aspects that can appear in a broad range of intrinsic fingerprints. The first aspect concerns intrinsic fingerprints that are designed to identify a particular component in the processing chain. Such fingerprints are potentially subject to changes due to input content variations and/or post-processing, and it is desirable to ensure their identifiability in such situations. Taking an image-based intrinsic fingerprinting technique for source camera model identification as a representative example, our investigations reveal that the fingerprints have a substantial dependency on image content. Such dependency limits the achievable identification accuracy, which is penalized by a mismatch between training and testing image content. To mitigate such a mismatch, we propose schemes to incorporate image content into training image selection and significantly improve the identification performance. We also consider the effect of post-processing against intrinsic fingerprinting, and study source camera identification based on imaging noise extracted from low-bit-rate compressed videos. While such compression reduces the fingerprint quality, we exploit different compression levels within the same video to achieve more efficient and accurate identification. The second aspect of resiliency addresses anti-forensics, namely, adversarial actions that intentionally manipulate intrinsic fingerprints. We investigate the cost-effectiveness of anti-forensic operations that counteract color interpolation identification. Our analysis pinpoints the inherent vulnerabilities of color interpolation identification, and motivates countermeasures and refined anti-forensic strategies. We also study the anti-forensics of an emerging space-time localization technique for digital recordings based on electrical network frequency analysis. Detection schemes against anti-forensic operations are devised under a mathematical framework. For both problems, game-theoretic approaches are employed to characterize the interplay between forensic analysts and adversaries and to derive optimal strategies. The third aspect regards the resilient and robust representation of intrinsic fingerprints for multiple forensic identification tasks. We propose to use the empirical frequency response as a generic type of intrinsic fingerprint that can facilitate the identification of various linear and shift-invariant (LSI) and non-LSI operations

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Ultrafast High-Energy Electron Diffraction Study of Photoexcited Bismuth Nanoclusters by Femtosecond Laser Pulses

The advancement in ultrafast electron crystallography (UEC) over the past few decades facilitated the study of structural dynamics in all phases of matter induced by femtosecond laser pulses. This technique became very powerful when the spatial resolution was combined with the temporal resolution, and succeeded in studying chemical reactions by ultrafast electron diffraction, bulk crystal phonons and melting by X-ray diffraction. In this dissertation, I demonstrate the uniqueness of UEC and its potential in monitoring in real time the structural dynamics of bismuth (Bi) nanoclusters and islands induced by femtosecond laser pulses. Our approach to accomplish this task includes building a time-resolved high energy electron diffraction setup that is capable of delivering high energy and short electron pulses, less than 3 ps, which will facilitate the real time measurement of the Bragg diffraction ring intensity, shift in the peak position and the diffraction ring full width at half maximum (FWHM) at different delay times with respect to the femtosecond excitation. Additionally, the temperature evolution of the same parameters, intensity, position and FWHM of the diffraction peaks, was monitored by using conventional direct current heating stage. Another task was accomplished in which I utilized the pump-probe ultrafast electron diffraction setup that I built and tested with picoseconds laser pulses in PERI lab - Old Dominion University, transferred later to the Applied Research Center where a femtosecond laser system was used to characterize the transient effects induced in Bi nanoclusters due to femtosecond laser excitation. The sample under consideration is excited by a femtosecond laser pulses with moderate fluence just to induce an observable change in the diffraction pattern and far from sample damage. The femtosecond laser pulses induce changes in the charge carrier distribution function of Bi nanoclusters, which leads to a disturbance in the lattice potential and drives the solid-liquid phase transformation. The melting is detected as decrease in the integrated intensity of the Bragg peaks with time delay. Another interesting behavior is observed in these experiments in which a lattice contraction following femtosecond laser excitation and proceeding over a time period of ∼ 6ps precedes the lattice expansion in Bi (012) planes. Again, the electronic excitation, here, plays an important role in inducing a sudden change in the interatomic forces which leads to A1g phonon excitation. Due to the limited resolution of our system (2–3 ps) we were not able to detect the A1g oscillation frequency/wavelength, but its effects which appear as lattice contraction upon its decay can be seen from the temporal evolution of the Bragg peak position over the time period, 0 \u3c t \u3c 6 ps. The incident laser fluence was not high enough to induce full melting, but was enough to induce partial lattice melting. This was observed as a gradual increase in the FWHM of the Bragg peaks as a function of delay time, i.e., formation of thin liquid layer which increases in size with time when the lattice temperature increases through electron-phonon and/or phonon-phonon relaxation. Also, the time evolution of the relative Bragg peak intensity, Δd/d and FWHM were monitored for Bi islands. Bi islands were prepared by annealing the as-deposited Bi thin film (5 nm, average coverage) solely by either raising its temperature slowly up to ∼ 525 K or with ultrafast laser pulses of fluence 0.8 mJ/cm² - 2.4 mJ/cm²

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters