A feature-based approach for digital camera identification using photo-response non-uniformity noise. (29th April 2021)

• Record Type: Journal Article
• Authors: Borole, Megha; Kolhe, Satish R.
• Is Part Of: International journal of computational vision and robotics. Volume 11:Number 4(2021)
• Page Start: 374
• Page End: 384
• Publication Date: 2021-04-29
• Subjects: camera identification -- photo-response non-uniformity -- PRNU -- fuzzy min-max neural network -- FMNN; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

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.