SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep Learning
This work addresses source camera attribution for forensic analysis, offering incremental improvements over prior methods.
The paper tackled the problem of source camera identification by improving sensor pattern noise extraction from single images, resulting in a deep learning approach that outperforms existing denoising filters and is validated across multiple public datasets.
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.