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Digital Image Forensics Based on CFA Interpolation Feature and Gaussian Mixture Model

Digital Image Forensics Based on CFA Interpolation Feature and Gaussian Mixture Model

Xinyi Wang, Shaozhang Niu, Jiwei Zhang
Copyright: © 2019 |Volume: 11 |Issue: 2 |Pages: 12
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781522565154|DOI: 10.4018/IJDCF.2019040101
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MLA

Wang, Xinyi, et al. "Digital Image Forensics Based on CFA Interpolation Feature and Gaussian Mixture Model." IJDCF vol.11, no.2 2019: pp.1-12. http://doi.org/10.4018/IJDCF.2019040101

APA

Wang, X., Niu, S., & Zhang, J. (2019). Digital Image Forensics Based on CFA Interpolation Feature and Gaussian Mixture Model. International Journal of Digital Crime and Forensics (IJDCF), 11(2), 1-12. http://doi.org/10.4018/IJDCF.2019040101

Chicago

Wang, Xinyi, Shaozhang Niu, and Jiwei Zhang. "Digital Image Forensics Based on CFA Interpolation Feature and Gaussian Mixture Model," International Journal of Digital Crime and Forensics (IJDCF) 11, no.2: 1-12. http://doi.org/10.4018/IJDCF.2019040101

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Abstract

According to the characteristics of the color filter array interpolation in a camera, an image splicing forgery detection algorithm based on bi-cubic interpolation and Gaussian mixture model is proposed. The authors make the assumption that the image is acquired using a color filter array, and that tampering removes the artifacts due to a demosaicing algorithm. This article extracts the image features based on the variance of the prediction error and create image feature likelihood map to detect and locate the image tampered areas. The experimental results show that the proposed method can detect and locate the splicing tampering areas precisely. Compared with bi-linear interpolation, this method can reduce the prediction error and improve the detection accuracy.