Feature Sets for Screenshot Detection

Abstract

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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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2013
Accession Number
ADA585612

Entities

People

  • Lauren Sharpe

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Automata Theory
  • Change Detection
  • Computational Science
  • Computer Programming
  • Computer Vision
  • Computers
  • Data Mining
  • Detectors
  • Digital Images
  • Image Processing
  • Information Science
  • Machine Learning
  • Network Science
  • Operating Systems
  • Pattern Recognition
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Database Systems and Applications
  • Mathematics or Statistics