Photo Response Non-Uniformity Extraction and Detection
Benjamin Loison, Quentin Bammey, Rafael Grompone von Gioi, Pablo Musé, Marina Gardella
⚠ This is a preprint. It may change before it is accepted for publication.

Abstract

Identifying the specific device model or instance that captured a given image is crucial in various forensic applications. In this context, the Photo Response Non-Uniformity (PRNU) plays a central role. The PRNU is a component of the image noise caused by the manufacturing process of digital cameras. Each pixel sensor responds slightly differently to the same light stimulus, under- or over-estimating the incoming signal in a way that depends on subtle, fixed imperfections unique to that sensor. This pixel-level variation creates a characteristic pattern that acts as a fingerprint of the camera. Because this fingerprint is intrinsic to the physical sensor, it remains consistent across different images taken with the same device. Furthermore, even among cameras of the same model, which share identical hardware and image processing pipelines, the PRNU pattern is distinct. The ability to extract and compare these fingerprints enables the attribution of an image to a specific device. The goal of this article is to provide a unified and transparent implementation of PRNU-based source camera identification methods, including several state-of-the-art approaches from the literature. We aim to systematically compare these methods, with particular emphasis on reproducibility, clarity of implementation, and ease of experimentation. Our objective is to deepen the understanding of the underlying assumptions, design choices, and processing steps involved in PRNU estimation and matching.

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