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Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics

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DOI: 10.23977/jipta.2024.070106 | Downloads: 14 | Views: 188

Author(s)

Ao Xiang 1, Jingyu Zhang 2, Qin Yang 3, Liyang Wang 4, Yu Cheng 5

Affiliation(s)

1 School of Computer Science & Engineering (School of Cybersecurity), Digital Media Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
2 The Division of the Physical Sciences, The University of Chicago, Analytics, Chicago, IL, USA
3 School of Integrated Circuit Science and Engineering (Exemplary School of Microelectronics), Microelectronics Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
4 Olin Business School, Washington University in St. Louis, Finance, St. Louis, MO, USA
5 The Fu Foundation School of Engineering and Applied Science, Operations Research, Columbia University, New York, NY, USA

Corresponding Author

Ao Xiang

ABSTRACT

With the development and widespread application of digital image processing technology, image splicing has become a common method of image manipulation, raising numerous security and legal issues. This paper introduces a new splicing image detection algorithm based on the statistical characteristics of natural images, aimed at improving the accuracy and efficiency of splicing image detection. By analyzing the limitations of traditional methods, we have developed a detection framework that integrates advanced statistical analysis techniques and machine learning methods. The algorithm has been validated using multiple public datasets, showing high accuracy in detecting spliced edges and locating tampered areas, as well as good robustness. Additionally, we explore the potential applications and challenges faced by the algorithm in real-world scenarios. This research not only provides an effective technological means for the field of image tampering detection but also offers new ideas and methods for future related research.

KEYWORDS

Image tampering detection; Natural image; statistical characteristics; Machine learning; Digital image processing

CITE THIS PAPER

Ao Xiang, Jingyu Zhang, Qin Yang, Liyang Wang, Yu Cheng, Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics. Journal of Image Processing Theory and Applications (2024) Vol. 7: 43-52. DOI: http://dx.doi.org/10.23977/jipta.2024.070106.

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