Forensic Analysis of Digital Image Tampering

The use of digital photography has increased over the past few years, a trend which opens the door for new and creative ways to forge images. The manipulation of images through forgery influences the perception an observer has of the depicted scene, potentially resulting in ill consequences if created with malicious intentions. This poses a need to verify the authenticity of images originating from unknown sources in absence of any prior digital watermarking or authentication technique. This research explores the holes left by existing research; specifically, the ability to detect image forgeries created using multiple image sources and specialized methods tailored to the popular JPEG image format. In an effort to meet these goals, this thesis presents four methods to detect image tampering based on fundamental image attributes common to any forgery. These include discrepancies in 1) lighting and 2) brightness levels, 3) underlying edge inconsistencies, and 4) anomalies in JPEG compression blocks. Overall, these methods proved encouraging in detecting image forgeries with an observed accuracy of 60% in a completely blind experiment containing a mixture of 15 authentic and forged images

On the Scientific Maturity of Digital Forensics Research

In this paper we transfer a well-known grade schema of scientific maturity from the domain of software engineering into the domain of digital forensics research. On the basis of this maturity schema and its grades we classify the current state of maturity in the research field of digital forensics, and we argue for more efforts towards higher levels of scientificness in this still new field of research.http://link.springer.com/chapter/10.1007/978-3-642-41148-9_3mv201

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

Air Force Institute of Technology Research Report 2005

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

Identifying Image Manipulation Software from Image Features

As technology steadily increases in the field of image manipulation, determining which software was used to manipulate an image becomes increasingly complex for law enforcement and intelligence agencies. To combat this difficult problem, new techniques that examine the artifacts left behind by a specific manipulation are converted to features for classification. This research implemented four preexisting image manipulation detection techniques into a framework of modules: Two-Dimensional Second Derivative, One-Dimensional Zero Crossings, Quantization Matrices Identification, and File Metadata analysis. The intent is the creation of a framework to develop a capability to determine which specific image manipulation software program manipulated an image. The determination is based on each image manipulation software program having implemented the manipulation algorithms differently. These differences in the implementation will leave behind different artifacts in the resultant image. Experimental results demonstrate the framework\u27s ability to identify from the 48 combinations of image manipulation software programs, scaling, and the algorithm used with a true positive rate of 0.54, false positive rate of 0.01, and a Kappa statistic of 0.53 for Joint Photographic Experts Group (JPEG). The results for Tagged Image File Format (TIFF) images were a true positive rate of 0.53, false positive rate of 0.01, and a Kappa statistic of 0.52

Forensic Analysis of Digital Image Tampering

The use of digital photography has increased over the past few years, a trend which opens the door for new and creative ways to forge images. The manipulation of images through forgery influences the perception an observer has of the depicted scene, potentially resulting in ill consequences if created with malicious intentions. This poses a need to verify the authenticity of images originating from unknown sources in absence of any prior digital watermarking or authentication technique. This research explores the ability to detect image forgeries created using multiple image sources and specialized methods tailored to the popular JPEG image format. Four methods are presented for detection of image tampering based on fundamental image attributes common to any forgery. These include discrepancies in (i) lighting levels, (ii) brightness levels, (iii) underlying edge inconsistencies, and (iv) anomalies in JPEG compression blocks. These methods detected image forgeries with an observed accuracy of 60% in a completely blind experiment containing a mixture of 15 authentic and forged images