Browsing by Author "Altinişik, Enes"

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PRNU Estimation From Encoded Videos Using Block-Based Weighting
(Society for Imaging Science and Technology, 2021) Altinişik, Enes; Taşdemir, Kasím; Sencar, Hüsrev Taha
Estimating the photo-response non-uniformity (PRNU) of an imaging sensor from videos is a challenging task due to complications created by several processing steps in the camera imaging pipeline. Among these steps, video coding is one of the most disruptive to PRNU estimation because of its lossy nature. Since videos are always stored in a compressed format, the ability to cope with the disruptive effects of encoding is central to reliable attribution. In this work, by focusing on the block-based operation of widely used video coding standards, we present an improved approach to PRNU estimation that exploits this behavior. To this purpose, several PRNU weighting schemes that utilize block-level parameters, such as encoding block type, quantization strength, and rate-distortion value, are proposed and compared. Our results show that the use of the coding rate of a block serves as a better estimator for the strength of PRNU with almost three times improvement in the matching statistic at low to medium coding bitrates as compared to the basic estimation method developed for photos. © 2021 Elsevier B.V., All rights reserved.
Citation - WoS: 12
Citation - Scopus: 19
Extracting PRNU Noise From H.264 Coded Videos
(European Signal Processing Conference, EUSIPCO, 2018) Altinişik, Enes; Taşdemir, Kasím; Sencar, Hüsrev Taha
Every device equipped with a digital camera has a unique identity. This phenomenon is essentially due to a systematic noise component of an imaging sensor, known as photo-response non-uniformity (PRNU) noise. An imaging sensor inadvertently introduces this noise pattern to all media captured by that imaging sensor. The procedure for extracting PRNU noise has been well studied in the context of photographic images, however, its extension to video has so far been neglected. In this work, considering H.264 coding standard, we describe a procedure to extract sensor fingerprint from non-stabilized videos. The crux of our method is to remove a filtering procedure applied at the decoder to reduce blockiness and to use macroblocks selectively when estimating PRNU noise pattern. Results show that our method has a potential to improve matching performance significantly. © 2019 Elsevier B.V., All rights reserved.