Prnu fingerprints remain identifiable on modern smartphone images
A New Hope for PRNU on Modern Smartphones? Evidence That Device-Specific Fingerprints Survive Computational Photography
Computer Vision and Pattern Recognition
Summary
It has been hard to tell which modern smartphone took a picture because the usual method, PRNU, sometimes gives confusing results. The authors show that part of the problem is how comparisons are done, not the fingerprint itself. They designed a new way to compare PRNU fingerprints, called ΔsPCE, which better tells apart individual phones even of the same model. This means smartphones still have unique camera marks, but you need smarter comparisons to find them.
What this means in practice
- •For digital forensics teams: Identify the specific smartphone that took a photo by using ΔsPCE comparisons to distinguish devices of the same model more reliably.
- •For mobile security engineers: Improve authentication and tracking mechanisms by confirming device-specific camera fingerprints survive modern image processing.
Tested on one dataset.
Authors
Benjamin Loison, Quentin Bammey, Thibaud Ehret, Rafael Grompone von Gioi, Pablo Musé, Marina Gardella
Abstract
Photo Response Non-Uniformity (PRNU) is widely regarded as the most reliable marker for source camera identification. Recent studies, however, report high correlations between images and fingerprints from different modern smartphones, which calls into question the discriminative power of PRNU on these devices. Using 30 recent smartphones under controlled acquisition conditions (e.g., no digital zoom), we investigate whether the fingerprint itself is at fault or the way it is compared. We find that a component shared by all fingerprints of the same model inflates the standard signed peak-to-correlation energy (sPCE). To counter this, we propose $Δ$sPCE: the sPCE between a query image and a candidate camera's fingerprint, minus the mean sPCE between that image and the fingerprints of the remaining candidates, in the spirit of T-norm score normalization. $Δ$sPCE improves attribution accuracy, suggesting that the device-specific PRNU signal is still present. Although the limited dataset size and the dependence of $Δ$sPCE on the candidate camera set currently restrict operational applicability, these results suggest that PRNU-based attribution of modern smartphones remains feasible when fingerprints are compared appropriately.