  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.
