PRNU Estimation from Encoded Videos Using Block-Based Weighting
This work addresses the problem of reliable sensor attribution from compressed videos for forensic analysis, representing an incremental improvement in handling encoding effects.
The paper tackled the challenge of estimating photo-response non-uniformity (PRNU) from encoded videos, which is disrupted by lossy video coding, and resulted in a nearly threefold improvement in matching statistics at low to medium bitrates compared to basic photo-based methods.
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.