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

Autor: Enes Altinisik, Kasim Tasdemir, Husrev Taha Sencar
Přispěvatelé: TOBB ETU, Faculty of Engineering, Department of Computer Engineering, TOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Sencar, Hüsrev Taha, AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Computer Networks and Communications
Computer science
0211 other engineering and technologies
Macroblock
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Context (language use)
02 engineering and technology
H.264/H.265 encoding & decoding
cameras
FOS: Electrical engineering
electronic engineering
information engineering
Computer vision
Image sensor
Safety
Risk
Reliability and Quality
pipelines
photo-response non-uniformity (PRNU)
021110 strategic
defence & security studies
reliability
business.industry
Image and Video Processing (eess.IV)
Fingerprint (computing)
Perspective (graphical)
Electrical Engineering and Systems Science - Image and Video Processing
video source attribution
Metric (mathematics)
standards
Artificial intelligence
business
video compression
Estimation
Decoding methods
Data compression
Popis: This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 116E273. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Luisa Verdoliva. 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. Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) 116E273
Databáze: OpenAIRE
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