Identification of Source Camera by Amalgamation of PRNU and Noise Print Using Dimensionality Expansive Residual Network

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Published Nov 26, 2022
https://doi.org/10.47164/ijngc.v13i5.919
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Volume 13, Special Issue 5, November 2022

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Shubham Anjankar
Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Somesh Telang
Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Khushalsingh Bharadwaj
Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Richa Khandelwal
Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India

Abstract

It might be challenging in the field of image forensics to identify the source camera of a picture. This research
proposes a noise adaptable convolutional neural network-based technique for camera identification. For camera
identification, the suggested solution combines Photo Response Non-Uniformity (PRNU) noise and Noiseprint.
Three parallel dimensionality expanded residual networks with convolutional layers of kernel size 1x1 were put
together for enhanced feature extraction. The experiment mentioned above uses pictures from the ”Vision Dataset”
as its subject matter. The experimental findings demonstrate the effectiveness of the suggested methodology in
identifying the source camera at the brand, model, and device levels. When two of the three networks were fed
with PRNU and one with noiseprint, the best performance was obtained.

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Anjankar, S., Telang, S., Bharadwaj, K., & Khandelwal, R. (2022). Identification of Source Camera by Amalgamation of PRNU and Noise Print Using Dimensionality Expansive Residual Network. International Journal of Next-Generation Computing, 13(5). https://doi.org/10.47164/ijngc.v13i5.919
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