Feature sets for screenshot detection
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As digital media capacity continues to increase and the cost continues to decrease, digital forensic examiners need progressively more efficient, effective, and tailored tools in order to perform useful media triage. This thesis documents the development of feature sets for classifying images as either screenshots or non-screenshots. Using linear- and intensity-based image information we developed the first (to our knowledge) screenshot detection algorithm. Four feature sets were developed and combinations of these feature sets were tested, with the best results achieving an F-score of 0.98 in ten-fold cross-validation. Requiring less than 0.18 seconds to analyze and classify an image, this is a critical contribution to the state-of-the-art of media forensics.
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