References & Citations
Computer Science > Computer Vision and Pattern Recognition
Title: Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics
(Submitted on 25 Apr 2024 (v1), last revised 26 Apr 2024 (this version, v2))
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.
Submission history
From: Ao Xiang [view email][v1] Thu, 25 Apr 2024 02:28:16 GMT (666kb)
[v2] Fri, 26 Apr 2024 07:45:58 GMT (584kb)
Link back to: arXiv, form interface, contact.