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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Veröffentlicht in:Multimedia tools and applications Jg. 78; H. 7; S. 8225 - 8245
Hauptverfasser: Ding, Feng, Shi, Yuxi, Zhu, Guopu, Shi, Yun-Qing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2019
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-6807-6