Title: A feature-based approach for digital camera identification using photo-response non-uniformity noise
Authors: Megha Borole; Satish R. Kolhe
Addresses: School of Computer Sciences, Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon, Maharashtra-425002, India ' School of Computer Sciences, Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon, Maharashtra-425002, India
Abstract: Source camera identification of an image is an emerging field of digital forensics. To identify the source camera through which the image is captured, photo-response non-uniformity (PRNU) noise is used as a camera fingerprint, as it is a unique characteristic that distinguishes images taken from the similar cameras. This paper presents a feature-based approach to identify the source camera. The input image is denoised using the denoising filter and from this denoised image, PRNU noise pattern is extracted. These PRNU noise patterns are represented by Hu's invariants, which are perpetual under image scaling, translation and rotation. These features are fed to fuzzy min-max neural network (FMNN) for training and classification for digital camera identification. The proposed approach has the ability to identify the cameras capturing the same scene.
Keywords: camera identification; photo-response non-uniformity; PRNU; fuzzy min-max neural network; FMNN.
International Journal of Computational Vision and Robotics, 2021 Vol.11 No.4, pp.374 - 384
Accepted: 18 Mar 2020
Published online: 29 Apr 2021 *