Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3858
Title: Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
Authors: Altınışık, Enes
Tasdemir, Kasım
Sencar, Hüsrev Taha
Keywords: Estimation
cameras
video compression
reliability
pipelines
standards
photo-response non-uniformity (PRNU)
video source attribution
H.264/H.265 encoding & decoding
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Altinisik, E., Tasdemir, K. and Sencar, H. T. (2019). Mitigation of h. 264 and h. 265 video compression for reliable prnu estimation. IEEE Transactions on information forensics and security, 15, 1557-1571.
Abstract: The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to generation of videos remain largely untackled. With this perspective, this work introduces methods to mitigate disruptive effects of widely deployed H.264 and H.265 video compression standards on PRNU estimation. Our approach involves an intervention in the decoding process to eliminate a filtering procedure applied at the decoder to reduce blockiness. It also utilizes decoding parameters to develop a weighting scheme and adjust the contribution of video frames at the macroblock level to PRNU estimation process. Results obtained on videos captured by 28 cameras show that our approach increases the PRNU matching metric up to more than five times over the conventional estimation method tailored for photos. Tests on a public dataset also verify that the proposed method improves the attribution performance by increasing the accuracy and allowing the use of smaller length videos to perform attribution. © 2005-2012 IEEE.
URI: https://hdl.handle.net/20.500.11851/3858
https://ieeexplore.ieee.org/document/8854840
ISSN: 1556-6013
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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