Ph.D. Dissertation Defense: Adi Al Hajj Ahmad

Friday, September 2, 2016
11:00 a.m.
Room 2211, Kim Bldg.
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: Ph.D. Dissertation Defense

 

NAME: Adi Al Hajj Ahmad

 

Advisory Committee:

Professor Min Wu, Chair/Advisor

Professor Gang Qu

Professor Behtash Babadi

Dr. Gwenaël Doërr

Professor Miao Yu, Dean’s representative

 

Date/Time: Friday, September 2, 2016 at 11:00am

 

Place: Room 2211, Kim Engineering Building


Title: Intrinsically Embedded Signatures for Multimedia Forensics

 

Abstract:

This dissertation examines the use of signatures that are intrinsically embedded in media recordings for multimedia forensic studies and applications. 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 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 50 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 the recording 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 ENF estimation through spectrum combining approach that exploits the presence of the 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 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 analyzing solely 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.

 

 

Audience: Graduate  Faculty 

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