Discrimination of Computer Generated and Photographic Images Based on CQWT Quaternion Markov Features
Document Type
Article
Publication Date
2-1-2019
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.
Identifier
85052923117 (Scopus)
Publication Title
International Journal of Pattern Recognition and Artificial Intelligence
External Full Text Location
https://doi.org/10.1142/S0218001419540077
ISSN
02180014
Issue
2
Volume
33
Fund Ref
National Science Foundation
Recommended Citation
Wang, Jinwei; Li, Ting; and Shih, Frank Y., "Discrimination of Computer Generated and Photographic Images Based on CQWT Quaternion Markov Features" (2019). Faculty Publications. 7814.
https://digitalcommons.njit.edu/fac_pubs/7814
