Discrimination of Computer Generated and Photographic Images Based on CQWT Quaternion Markov Features

Jinwei Wang, Ting Li, Frank Y. Shih

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, an effective method based on the color quaternion wavelet transform (CQWT) for image forensics is proposed. Compared to discrete wavelet transform (DWT), the CQWT provides more information, such as the quaternion's magnitude and phase measures, to discriminate between computer generated (CG) and photographic (PG) images. Meanwhile, we extend the classic Markov features into the quaternion domain to develop the quaternion Markov statistical features for color images. Experimental results show that the proposed scheme can achieve the classification rate of 92.70%, which is 6.89% higher than the classic Markov features.

Original languageEnglish (US)
Article number1954007
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume33
Issue number2
DOIs
StatePublished - Feb 1 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Markov
  • Quaternion
  • classification
  • forensics
  • wavelet transform

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