An Analysis of Optical Contributions to a Photo-Sensor's Ballistic Fingerprints

TL;DR

This paper introduces a new model that separates lens aberrations and temperature effects from sensor noise, enhancing the uniqueness of photo-sensor fingerprints for image provenance analysis.

Contribution

A novel additive signal model that isolates lens and temperature artefacts from sensor noise, improving the accuracy of sensor fingerprinting.

Findings

Model successfully separates lens artefacts from sensor noise.

Enhanced accuracy in sensor fingerprinting by accounting for optical and temperature biases.

Provides a theoretical framework for understanding light physics and temperature effects on sensor signals.

Abstract

Lens aberrations have previously been used to determine the provenance of an image. However, this is not necessarily unique to an image sensor, as lens systems are often interchanged. Photo-response non-uniformity noise was proposed in 2005 by Luk\'a\v{s}, Goljan and Fridrich as a stochastic signal which describes a sensor uniquely, akin to a "ballistic" fingerprint. This method, however, did not account for additional sources of bias such as lens artefacts and temperature. In this paper, we propose a new additive signal model to account for artefacts previously thought to have been isolated from the ballistic fingerprint. Our proposed model separates sensor level artefacts from the lens optical system and thus accounts for lens aberrations previously thought to be filtered out. Specifically, we apply standard image processing theory, an understanding of frequency properties relating to the physics of light and temperature response of sensor dark current to classify artefacts. This model enables us to isolate and account for bias from the lens optical system and temperature within the current model.