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Data Driven Video Source Camera Identification

Hopkins, Nicholas Christian
http://orcid.org/0000-0002-0043-4066
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1682701385574886

Abstract Details

2023, Doctor of Philosophy (Ph.D.), University of Dayton, Engineering.
Given a set of video imagery from unknown device provenance, video-based source camera identification (V-SCI) refers to a task of identifying which device collected the imagery. V-SCI techniques predominantly leverage photo response non-uniformity (PRNU) patterns extracted from digital video for device identification decisions. PRNU patterns function as device fingerprints and SCI methods using PRNU from digital still imagery (I-SCI) are relatively mature; however, advancements in video processing, namely electronic image stabilization (EIS) algorithms, degrade video extracted PRNU distinctiveness yielding a significant obstacle toward extending I-SCI performance to EIS processed video datasets. We provide a new, more relevant PRNU dataset, UDAYTON23VSCI, for V-SCI benchmarking in contrast to current publicly available datasets. To address the EIS V-SCI challenge, we present a data-driven approach to exploit PRNU signals derived from EIS video via ``device-specific'' neural networks implemented with a novel PRNU image training and transfer learning strategy. Results implementing our device-specific network approach on UDAYTON23VSCI and a leading publicly available dataset confirm the advantages of our approach over state of the art SCI methods. We provide a new PRNU computation approach via Log-noise PRNU estimation which overcomes multiplicative noise constraints inherent to PRNU patterns in imagery. We show our Log-noise PRNU estimation approach outperforms the current widely accepted PRNU estimation approach based on maximum likelihood estimation (MLE) in V-SCI task thus eliminating the need for MLE in computing PRNU. Lastly, by removing MLE PRNU computation requirement, we show our Log-noise PRNU estimation approach is a key contribution toward realizing a fully data driven end-to-end (E2E) network design for tackling EIS V-SCI.
Keigo Hirakawa (Advisor)
Barath Narayanan (Committee Member)
Partha Banerjee (Committee Member)
Vijayan Asari (Committee Member)
106 p.
Artificial Intelligence; Electrical Engineering
Video source camera identification, V-SCI, Camera device attribution, SCI, Electronic image stabilization, EIS, CNN SCI, Photo response non-uniformity, PRNU, Log-noise PRNU estimation, end to end fully data driven video SCI

Recommended Citations

Citations

  • Hopkins, N. C. (2023). Data Driven Video Source Camera Identification [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1682701385574886

    APA Style (7th edition)

  • Hopkins, Nicholas. Data Driven Video Source Camera Identification. 2023. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1682701385574886.

    MLA Style (8th edition)

  • Hopkins, Nicholas. "Data Driven Video Source Camera Identification." Doctoral dissertation, University of Dayton, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1682701385574886

    Chicago Manual of Style (17th edition)

dayton1682701385574886
52
© 2023, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.