A Preliminary Approach to Using PRNU Based Transfer Learning for Camera Identification

Sharan Seshadri, K. R. Akshatha, A. K. Karunakar, Kelvin Harrison Paul

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


In this paper, a preliminary method of source camera identification using transfer learning methods is proposed. Every image taken by a camera has a unique artefact, known as photo response non-uniformity (PRNU) noise that is manifested in the image, because of irregularities during the manufacturing process of the image sensor present in that camera. A convolutional neural network (CNN) is used here to extract the features pertaining to the noise residual of a specific camera, which can be later passed to a classifier. In this paper, we use a CNN architecture that has weights that have been pre-trained to extract the residual features. This method is called transfer learning and is used after denoising the images in the dataset. The noise residual features are extracted by the transfer learning model and are then passed to a Support Vector Machine (SVM) classifier. Despite the simplicity of this approach, the results obtained are favorable compared to the existing methods, especially considering the small dataset used.

Original languageEnglish
Title of host publicationAdvances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC
EditorsKohei Arai, Supriya Kapoor
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783030177973
Publication statusPublished - 01-01-2020
EventComputer Vision Conference, CVC 2019 - Las Vegas, United States
Duration: 25-04-201926-04-2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


ConferenceComputer Vision Conference, CVC 2019
Country/TerritoryUnited States
CityLas Vegas

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • General Computer Science


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