Testing Robustness of Camera Fingerprint (PRNU) Detectors

TL;DR

This paper evaluates the robustness of PRNU-based camera fingerprint detectors by designing and testing various minimal-impact attacks to assess their effectiveness in fooling source camera identification systems.

Contribution

It introduces a set of attack methods targeting PRNU detectors and measures their success, providing insights into the vulnerabilities of current forensic techniques.

Findings

Certain attacks significantly reduce detection accuracy

PRNU detectors are vulnerable to specific minimal-impact manipulations

Results highlight the need for more robust camera fingerprinting methods

Abstract

In the field of forensic imaging, it is important to be able to extract a 'camera fingerprint' from one or a small set of images known to have been taken by the same camera. Ideally, that fingerprint would be used to identify an individual source camera. Camera fingerprint is based on certain kind of random noise present in all image sensors that is due to manufacturing imperfections and thus unique and impossible to avoid. PRNU (Photo-Response Non-Uniformity) has become the most widely used method for SCI (Source Camera Identification). In this paper, we design a set of 'attacks' to a PRNU based SCI system and we measure the success of each method. We understand an attack method as any processing that alters minimally image quality and that is designed to fool PRNU detectors (or, generalizing, any camera fingerprint detector). The PRNU based SCI system was taken from an outstanding reference that is publicly available.