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

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