A machine learning-based approach for picture acquisition timeslot prediction using defective pixels

Farah Nafees Ahmed*, Fouad Khelifi, Ashref Lawgaly, Ahmed Bouridane

*Corresponding author for this work

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Estimating the acquisition time of digital photographs is a challenging task in temporal image forensics, but the application is highly demanded for establishing temporal order among individual pieces of evidence and deduce the causal relationship of events in a court case. The forensic investigator needs to identify the timeline of events and look for some patterns to gain a clear overview of activities associated with a crime. This paper aims to explore the presence of defective pixels over time for estimating the acquisition date of digital pictures. We propose a technique to predict the acquisition timeslots of digital pictures using a set of candidate defective pixels in non-overlapping image blocks. First, potential candidate defective pixels are determined through related pixel neighbourhood and two proposed features, called the local variation features to best fit in a machine learning model. The machine learning approach is used to model the temporal behaviour of camera sensor defects in each block using the scores obtained from individually trained pixel defect locations and fused in a majority voting method. Interestingly, timeslot estimation using individual blocks has been shown to be more accurate when virtual sub-classes corresponding to halved timeslots are first considered prior to the reconstruction step. Finally, the last stage of the system consists of the combination of block scores in a second majority voting operation to further enhance performance. Assessed on the NTIF image dataset, the proposed system has been shown to reach very promising results with an estimated accuracy between 88% and 93% and clear superiority over a related state-of-the-art system.
Original languageEnglish
Article number301311
Number of pages11
JournalForensic Science International: Digital Investigation
Early online date30 Oct 2021
Publication statusPublished - 1 Dec 2021


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