Smoothing identification for digital image forensics

With the explosive development in digital techniques, ordinary people without professional training are capable to edit digital images with applications. As a common image processing manipulation, smoothing is important in editing digital images for denoising and producing blur effect. Besides, in r...

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Vydané v:	Multimedia tools and applications Ročník 78; číslo 7; s. 8225 - 8245
Hlavní autori:	Ding, Feng, Shi, Yuxi, Zhu, Guopu, Shi, Yun-Qing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2019 Springer Nature B.V
Predmet:	Algorithms Computer Communication Networks Computer Science Data Structures and Information Theory Digital imaging Digital photography Digital techniques Feature extraction Forensic sciences Image manipulation Image processing Image processing systems Machine learning Multimedia Information Systems Noise reduction Smoothing Special Purpose and Application-Based Systems Support vector machines Image forensics Texture analysis Bilateral filter Smoothing detection Machine learning
ISSN:	1380-7501, 1573-7721
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Abstract	With the explosive development in digital techniques, ordinary people without professional training are capable to edit digital images with applications. As a common image processing manipulation, smoothing is important in editing digital images for denoising and producing blur effect. Besides, in recent years, people prefer to retouch images with smoothing algorithms to pursue better appearance. Hence it is required to expose such manipulations in digital image forensics. In this paper, a new scheme for detecting the operation of smoothing is proposed. The proposed scheme is based on analyzing the statistical property which can be considered as computation efficiently when compares to machine learning algorithms. Furthermore, a method for texture analysis is also proposed to specify the algorithm that used for smoothing. The second method adopt the features extracted from edge area. The features are fed into support vector machine for classification.
AbstractList	With the explosive development in digital techniques, ordinary people without professional training are capable to edit digital images with applications. As a common image processing manipulation, smoothing is important in editing digital images for denoising and producing blur effect. Besides, in recent years, people prefer to retouch images with smoothing algorithms to pursue better appearance. Hence it is required to expose such manipulations in digital image forensics. In this paper, a new scheme for detecting the operation of smoothing is proposed. The proposed scheme is based on analyzing the statistical property which can be considered as computation efficiently when compares to machine learning algorithms. Furthermore, a method for texture analysis is also proposed to specify the algorithm that used for smoothing. The second method adopt the features extracted from edge area. The features are fed into support vector machine for classification.
Author	Ding, Feng Shi, Yun-Qing Zhu, Guopu Shi, Yuxi
Author_xml	– sequence: 1 givenname: Feng surname: Ding fullname: Ding, Feng organization: Department of Electrical and Computer Engineering, New Jersey Institute of Technology – sequence: 2 givenname: Yuxi surname: Shi fullname: Shi, Yuxi organization: Department of Electrical and Computer Engineering, New Jersey Institute of Technology – sequence: 3 givenname: Guopu orcidid: 0000-0001-7956-5343 surname: Zhu fullname: Zhu, Guopu email: gp.zhu@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences – sequence: 4 givenname: Yun-Qing surname: Shi fullname: Shi, Yun-Qing organization: Department of Electrical and Computer Engineering, New Jersey Institute of Technology
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SubjectTerms	Algorithms Computer Communication Networks Computer Science Data Structures and Information Theory Digital imaging Digital photography Digital techniques Feature extraction Forensic sciences Image manipulation Image processing Image processing systems Machine learning Multimedia Information Systems Noise reduction Smoothing Special Purpose and Application-Based Systems Support vector machines
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Title	Smoothing identification for digital image forensics
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