dc.contributor.advisorWu, Minen_US
dc.contributor.authorGarg, Ravien_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.description.abstractIn the modern era, a vast quantities of digital information is available in the form of audio, image, video, and other sensor recordings. These recordings may contain metadata describing important information such as the time and the location of recording. As the stored information can be easily modified using readily available digital editing software, determining the authenticity of a recording has utmost importance, especially for critical applications such as law enforcement, journalism, and national and business intelligence. In this dissertation, we study novel environmental signatures induced by power networks, which are known as Electrical Network Frequency (ENF) signals and become embedded in multimedia data at the time of recording. ENF fluctuates slightly over time from its nominal value of 50 Hz/60 Hz. The major trend of fluctuations in the ENF remains consistent across the entire power grid, including when measured at physically distant geographical locations. We investigate the use of ENF signals for a variety of applications such as estimation/verification of time and location of a recording's creation, and develop a theoretical foundation to support ENF based forensic analysis. In the first part of the dissertation, the presence of ENF signals in visual recordings captured in electric powered lighting environments is demonstrated. The source of ENF signals in visual recordings is shown to be the invisible flickering of indoor lighting sources such as fluorescent and incandescent lamps. The techniques to extract ENF signals from recordings demonstrate that a high correlation is observed between the ENF fluctuations obtained from indoor lighting and that from the power mains supply recorded at the same time. Applications of the ENF signal analysis to tampering detection of surveillance video recordings, and forensic binding of the audio and visual track of a video are also discussed. In the following part, an analytical model is developed to gain an understanding of the behavior of ENF signals. It is demonstrated that ENF signals can be modeled using a time-varying autoregressive process. The performance of the proposed model is evaluated for a timestamp verification application. Based on this model, an improved algorithm for ENF matching between a reference signal and a query signal is provided. It is shown that the proposed approach provides an improved matching performance as compared to the case when matching is performed directly on ENF signals. Another application of the proposed model in learning the power grid characteristics is also explicated. These characteristics are learnt by using the modeling parameters as features to train a classifier to determine the creation location of a recording among candidate grid-regions. The last part of the dissertation demonstrates that differences exist between ENF signals recorded in the same grid-region at the same time. These differences can be extracted using a suitable filter mechanism and follow a relationship with the distance between different locations. Based on this observation, two localization protocols are developed to identify the location of a recording within the same grid-region, using ENF signals captured at anchor locations. Localization accuracy of the proposed protocols are then compared. Challenges in using the proposed technique to estimate the creation location of multimedia recordings within the same grid, along with efficient and resilient trilateration strategies in the presence of outliers and malicious anchors, are also discussed.en_US
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledElectrical Network Frequencyen_US
dc.subject.pquncontrolledInformation Forensicsen_US
dc.subject.pquncontrolledMultimedia Forensicsen_US


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