Intrinsically Embedded Signatures for Multimedia Forensics

Simple item page
dc.contributor.advisorWu, Minen_US
dc.contributor.authorAl Hajj Ahmad, Adi Mohammaden_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2017-01-25T06:36:16Z
dc.date.available2017-01-25T06:36:16Z
dc.date.issued2016en_US
dc.description.abstractThis dissertation examines the use of signatures that are intrinsically embedded in media recordings for studies and applications in multimedia forensics. These near-invisible signatures are fingerprints that are captured unintentionally in a recording due to influences from the environment in which it was made and the recording device that was used to make it. We focus on two types of such signatures: the Electric Network Frequency (ENF) signal and the flicker signal. The ENF is the frequency of power distribution networks and has a nominal value of 50Hz or 60Hz. The ENF fluctuates around its nominal value due to load changes in the grid. It is particularly relevant to multimedia forensics because ENF variations captured intrinsically in a media recording reflect the time and location related properties of the respective area in which it was made. This has led to a number of applications in information forensics and security, such as time-of-recording authentication/estimation and ENF-based detection of tampering in a recording. The first part of this dissertation considers the extraction and detection of the ENF signal. We discuss our proposed spectrum combining approach for ENF estimation that exploits the presence of ENF traces at several harmonics within the same recording to produce more accurate and robust ENF signal estimates. We also explore possible factors that can promote or hinder the capture of ENF traces in recordings, which is important for a better understanding of the real-world applicability of ENF signals. Next, we discuss novel real-world ENF-based applications proposed through this dissertation research. We discuss using the embedded ENF signal to identify the region-of-recording of a media signal through a pattern analysis and learning framework that distinguishes between ENF signals coming from different power grids. We also discuss the use of the ENF traces embedded in a video to characterize the video camera that had originally produced the video, an application that was inspired by our work on flicker forensics. The last part of the dissertation considers the flicker signal and its use in forensics. We address problems in the entertainment industry pertaining to movie piracy related investigations, where a pirated movie is formed by camcording media content shown on an LCD screen. The flicker signature can be inherently created in such a scenario due to the interplay between the back-light of an LCD screen and the recording mechanism of the video camera. We build an analytic model of the flicker, relating it to inner parameters of the video camera and the screen producing the video. We then demonstrate that solely analyzing such a pirated video can lead to the identification of the video camera and the screen that produced the video, which can be used as corroborating evidence in piracy investigations.en_US
dc.identifierhttps://doi.org/10.13016/M22Z5T
dc.identifier.urihttp://hdl.handle.net/1903/19067
dc.language.isoenen_US
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pqcontrolledComputer engineeringen_US
dc.subject.pquncontrolledENFen_US
dc.subject.pquncontrolledFlickeren_US
dc.subject.pquncontrolledMultimedia Forensicsen_US
dc.titleIntrinsically Embedded Signatures for Multimedia Forensicsen_US
dc.typeDissertationen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Dissertation_Adi_AlHajjAhmad_updated.pdf
Size:
7.38 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

UMD Theses and Dissertations
Electrical & Computer Engineering Theses and Dissertations