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Image sharpening detection based on multiresolution overshoot artifact analysis.

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Title: Image sharpening detection based on multiresolution overshoot artifact analysis.
Authors: Zhu, Nan1 nanzhu@stu.xidian.edu.cn, Deng, Cheng1 chdeng@mail.xidian.edu.cn, Gao, Xinbo1 xbgao@mail.xidian.edu.cn
Source: Multimedia Tools & Applications. Aug2017, Vol. 76 Issue 15, p16563-16580. 18p.
Subjects: Multiresolution time-domain method, Digital image processing, Digital electronics, Electronic data processing, Digitization
Abstract: With the wide use of sophisticated photo editing tools, digital image manipulation becomes very convenient, which makes the detection of image tampering significant. Image sharpening, which aims to enhance the contrast of edges in an image, is a ubiquitous image tampering operation. The detection of image sharpening can serve as a reliable clue for image forgery. In this paper, we propose a novel image sharpening detection method based on multiresolution overshoot artifact analysis (MOAA). By building the relationship between the overshoot artifact strength and the slope of a sharpened edge, we find that although undergoing the same sharpening operation, the edge with large slope will present a stronger overshoot artifact than the one with small slope. Based on this finding, we use the nonsubsampled contourlet transform (NSCT) to classify the image edge points into three categories, i.e., weak, middle and strong edge points and measure the overshoot artifact of each category respectively. A cascaded decision strategy is adopted to decide an image is sharpened or not. Experimental results on digital images with various sharpening operators demonstrate the superiority of our proposed method when compared with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Nan%22">Zhu, Nan</searchLink><relatesTo>1</relatesTo><i> nanzhu@stu.xidian.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Deng%2C+Cheng%22">Deng, Cheng</searchLink><relatesTo>1</relatesTo><i> chdeng@mail.xidian.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Xinbo%22">Gao, Xinbo</searchLink><relatesTo>1</relatesTo><i> xbgao@mail.xidian.edu.cn</i>
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  Data: With the wide use of sophisticated photo editing tools, digital image manipulation becomes very convenient, which makes the detection of image tampering significant. Image sharpening, which aims to enhance the contrast of edges in an image, is a ubiquitous image tampering operation. The detection of image sharpening can serve as a reliable clue for image forgery. In this paper, we propose a novel image sharpening detection method based on multiresolution overshoot artifact analysis (MOAA). By building the relationship between the overshoot artifact strength and the slope of a sharpened edge, we find that although undergoing the same sharpening operation, the edge with large slope will present a stronger overshoot artifact than the one with small slope. Based on this finding, we use the nonsubsampled contourlet transform (NSCT) to classify the image edge points into three categories, i.e., weak, middle and strong edge points and measure the overshoot artifact of each category respectively. A cascaded decision strategy is adopted to decide an image is sharpened or not. Experimental results on digital images with various sharpening operators demonstrate the superiority of our proposed method when compared with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Aug2017
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