SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep
Learning
- URL: http://arxiv.org/abs/2002.02927v1
- Date: Fri, 7 Feb 2020 17:55:28 GMT
- Title: SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep
Learning
- Authors: Matthias Kirchner and Cameron Johnson
- Abstract summary: 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.
Adeep learning approach can yield a more suitable extractor that leads to improved source attribution.
- Score: 1.370633147306388
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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