Information Forensics and Security: A quarter-century-long journey

Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era. The IEEE Signal Processing Society (SPS) has emerged as an important hub and leader in this area, and this article celebrates some landmark technical contributions. In particular, we highlight the major technological advances by the research community in some selected focus areas in the field during the past 25 years and present future trends

Multimedia Forensic Analysis via Intrinsic and Extrinsic Fingerprints

Digital imaging has experienced tremendous growth in recent decades, and digital images have been used in a growing number of applications. With such increasing popularity of imaging devices and the availability of low-cost image editing software, the integrity of image content can no longer be taken for granted. A number of forensic and provenance questions often arise, including how an image was generated; from where an image was from; what has been done on the image since its creation, by whom, when and how. This thesis presents two different sets of techniques to address the problem via intrinsic and extrinsic fingerprints. The first part of this thesis introduces a new methodology based on intrinsic fingerprints for forensic analysis of digital images. The proposed method is motivated by the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on the final output data. We present methods to identify these intrinsic fingerprints via component forensic analysis, and demonstrate that these traces can serve as useful features for such forensic applications as to build a robust device identifier and to identify potential technology infringement or licensing. Building upon component forensics, we develop a general authentication and provenance framework to reconstruct the processing history of digital images. We model post-device processing as a manipulation filter and estimate its coefficients using a linear time invariant approximation. Absence of in-device fingerprints, presence of new post-device fingerprints, or any inconsistencies in the estimated fingerprints across different regions of the test image all suggest that the image is not a direct device output and has possibly undergone some kind of processing, such as content tampering or steganographic embedding, after device capture. While component forensics is widely applicable in a number of scenarios, it has performance limitations. To understand the fundamental limits of component forensics, we develop a new theoretical framework based on estimation and pattern classification theories, and define formal notions of forensic identifiability and classifiability of components. We show that the proposed framework provides a solid foundation to study information forensics and helps design optimal input patterns to improve parameter estimation accuracy via semi non-intrusive forensics. The final part of the thesis investigates a complementing extrinsic approach via image hashing that can be used for content-based image authentication and other media security applications. We show that the proposed hashing algorithm is robust to common signal processing operations and present a systematic evaluation of the security of image hash against estimation and forgery attacks

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv) expression swap. For each manipulation group, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. In addition to the survey information, we also discuss open issues and future trends that should be considered to advance in the field

Anti- Forensics: The Tampering of Media

In the context of forensic investigations, the traditional understanding of evidence is changing where nowadays most prosecutors, lawyers and judges heavily rely on multimedia signs. This modern shift has allowed the law enforcement to better reconstruct the crime scenes or reveal the truth of any critical event.In this paper we shed the light on the role of video, audio and photos as forensic evidences presenting the possibility of their tampering by various easy-to-use, available anti-forensics softwares. We proved that along with the forensic analysis, digital processing, enhancement and authentication via forgery detection algorithms to testify the integrity of the content and the respective source of each, differentiating between an original and altered evidence is now feasible. These operations assist the court to attain higher degree of intelligibility of the multimedia data handled and assert the information retrieved from each that support the success of the investigation process