Three Dimensional Denoising Filter For Effective Source Smartphone Video Identification and Verification

Lawgaly, Ashref, Khelifi, Fouad, Bouridane, Ahmed, Al-Maaddeed, Somaya and Akbari, Younes (2022) Three Dimensional Denoising Filter For Effective Source Smartphone Video Identification and Verification. In: ICMLT 2022 : 2022 7th International Conference on Machine Learning Technologies (ICMLT). ACM, New York, pp. 124-130. ISBN 9781450395748

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Abstract

The field of digital image and video forensics has recently seen significant advances and has attracted attention from a growing number of researchers given the availability of imaging functionalities in most current multimedia devices at no cost including smartphones and tablets. Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. However, estimating the PRNU from smartphone videos can be a challenging process because of the lossy compression that digital videos normally undergo for various reasons in addition to other non-unique noise components that interfere with the video data. This paper presents a new filtering technique for PRNU estimation based on the three-dimensional discrete wavelet transform followed by a 3D wiener filter. The rationale is that the 3D filter can filter out the compression artifacts along the temporal dimension in a more effective way than simple averaging. Experimental results on a public video dataset captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the well-known two-dimensional wavelet-based Wiener approach.

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