# SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep Learning

arXiv: https://arxiv.org/abs/2002.02927 · HTML: https://mlsanity.com/paper/2002.02927/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [A comparison study of CNN denoisers on PRNU extraction](https://mlsanity.com/paper/2112.02858/) (2021)
- [Web Photo Source Identification based on Neural Enhanced Camera Fingerprint](https://mlsanity.com/paper/2302.09228/) (2023)
- [PRNU-Bench: A Novel Benchmark and Model for PRNU-Based Camera Identification](https://mlsanity.com/paper/2509.17581/) (2025)

## Abstract (arXiv)

We explore means to advance source camera identification based on sensor noise in a data-driven framework. Our focus is on improving the sensor pattern noise (SPN) extraction from a single image at test time. Where existing works suppress nuisance content with denoising filters that are largely agnostic to the specific SPN signal of interest, we demonstrate that a~deep learning approach can yield a more suitable extractor that leads to improved source attribution. A series of extensive experiments on various public datasets confirms the feasibility of our approach and its applicability to image manipulation localization and video source attribution. A critical discussion of potential pitfalls completes the text.