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A Universal Image Forensics of Smoothing Filtering

A Universal Image Forensics of Smoothing Filtering

Anjie Peng, Gao Yu, Yadong Wu, Qiong Zhang, Xiangui Kang
Copyright: © 2019 |Volume: 11 |Issue: 1 |Pages: 11
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781522565147|DOI: 10.4018/IJDCF.2019010102
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MLA

Peng, Anjie, et al. "A Universal Image Forensics of Smoothing Filtering." IJDCF vol.11, no.1 2019: pp.18-28. http://doi.org/10.4018/IJDCF.2019010102

APA

Peng, A., Yu, G., Wu, Y., Zhang, Q., & Kang, X. (2019). A Universal Image Forensics of Smoothing Filtering. International Journal of Digital Crime and Forensics (IJDCF), 11(1), 18-28. http://doi.org/10.4018/IJDCF.2019010102

Chicago

Peng, Anjie, et al. "A Universal Image Forensics of Smoothing Filtering," International Journal of Digital Crime and Forensics (IJDCF) 11, no.1: 18-28. http://doi.org/10.4018/IJDCF.2019010102

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Abstract

Digital image smoothing filtering operations, including the average filtering, Gaussian filtering and median filtering are always used to beautify the forged images. The detection of these smoothing operations is important in the image forensics field. In this article, the authors propose a universal detection algorithm which can simultaneously detect the average filtering, Gaussian low-pass filtering and median filtering. Firstly, the high-frequency residuals are used as being the feature extraction domain, and then the feature extraction is established on the local binary pattern (LBP) and the autoregressive model (AR). For the LBP model, the authors exploit that both of the relationships between the central pixel and its neighboring pixels and the relationships among the neighboring pixels are differentiated for the original images and smoothing filtered images. A method is further developed to reduce the high dimensionality of LBP-based features. Experimental results show that the proposed detector is effective in the smoothing forensics, and achieves better performance than the previous works, especially on the JPEG images.