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

Jinwei Wang; Ting Li; Frank Y. Shih

doi:10.1142/S0218001419540077

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

Jinwei Wang, Ting Li, Frank Y. Shih

Computer Science

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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 language	English (US)
Article number	1954007
Journal	International Journal of Pattern Recognition and Artificial Intelligence
Volume	33
Issue number	2
DOIs	https://doi.org/10.1142/S0218001419540077
State	Published - 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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10.1142/S0218001419540077

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title = "Discrimination of Computer Generated and Photographic Images Based on CQWT Quaternion Markov Features",

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.",

keywords = "Markov, Quaternion, classification, forensics, wavelet transform",

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AU - Wang, Jinwei

AU - Li, Ting

AU - Shih, Frank Y.

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Y1 - 2019/2/1

N2 - 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.

AB - 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.

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KW - Quaternion

KW - classification

KW - forensics

KW - wavelet transform

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

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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