Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation

Creative Commons License

Altinisik E., TAŞDEMİR K., SENCAR H. T.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol.15, pp.1557-1571, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 15
  • Publication Date: 2020
  • Doi Number: 10.1109/tifs.2019.2945190
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1557-1571
  • Abdullah Gül University Affiliated: Yes


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.