A Machine Learning Approach to Approximate the Age of a Digital Image

Research output: Contribution to conferencePaperpeer-review


In-field image sensor defects develop almost continually over a camera’s lifetime. Since these defects accumulate over time, a forensic analyst can approximate the age of an image under investigation based on the defects present. In this context, the temporal accuracy of the approximation is bounded by the different defect onset times. Thus, the approximation of the image age based on in-field sensor defects can be regarded as a multi-class classification problem. In this paper, we propose to utilize two well-known machine learning techniques (i.e. a Naive Bayes
Classier and a Support Vector Machine) to solve this problem. The accuracy of each technique is empirically evaluated by conducting several experiments, and the results are compared to the current state-of-the art in this field. In addition, the prediction results are assessed individually for each class.
Original languageEnglish
Publication statusPublished - 2021
EventInternational Workshop on Digital-forensics and Watermarking - online, Melbourne, Australia
Duration: 25 Nov 202027 Nov 2020
Conference number: 19


ConferenceInternational Workshop on Digital-forensics and Watermarking
Abbreviated titleIWDW 2020
Internet address

Fields of Science and Technology Classification 2012

  • 102 Computer Sciences

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