PRNU Estimation from Encoded Videos Using Block-Based Weighting

TL;DR

This paper introduces a novel block-based weighting method for estimating sensor PRNU from compressed videos, significantly improving attribution accuracy by leveraging coding parameters.

Contribution

It proposes new PRNU weighting schemes based on video coding block parameters, enhancing estimation robustness against lossy compression effects.

Findings

Almost three times improvement in matching statistic at low to medium bitrates.

Outperforms basic PRNU estimation methods for videos.

Effective use of coding rate as a PRNU strength estimator.

Abstract

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.