Source Camera Attribution of Multi-Format Devices

TL;DR

This paper investigates how different camera resizing techniques affect PRNU-based source attribution and proposes an algorithm to improve matching accuracy across various media formats.

Contribution

It introduces the Ratio of Alignment metric to quantify sensor element sharing and develops an algorithm for cross-format camera source attribution considering resizing methods.

Findings

Ratio of Alignment varies with resizing techniques

Proposed algorithm improves matching accuracy

Enhanced computational efficiency in source attribution

Abstract

Photo Response Non-Uniformity (PRNU) based source camera attribution is an effective method to determine the origin camera of visual media (an image or a video). However, given that modern devices, especially smartphones, capture images, and videos at different resolutions using the same sensor array, PRNU attribution can become ineffective as the camera fingerprint and query visual media can be misaligned. We examine different resizing techniques such as binning, line-skipping, cropping and scaling that cameras use to downsize the raw sensor image to different media. Taking such techniques into account, this paper studies the problem of source camera attribution. We define the notion of Ratio of Alignment, which is a measure of shared sensor elements among spatially corresponding pixels within two media objects resized with different techniques. We then compute the Ratio of Alignment between the different combinations of three common resizing methods under simplified conditions and experimentally validate our analysis. Based on the insights drawn from the different techniques used by cameras and the RoA analysis, the paper proposes an algorithm for matching the source of a video with an image and vice versa. We also present an efficient search method resulting in significantly improved performance in matching as well as computation time.