Image splicing localization using noise distribution characteristic

Abstract

Image splicing/compositing is common content tampering operation. In this work, we devote to improve the detection accuracy of the splicing/compositing attack for image, and propose an effective image splicing localization method based on the noise distribution characteristic in image. Firstly, the test image is divided into non-overlapping blocks by using an improved simple linear iterative clustering (SLIC) algorithm. Then block-wise local noise level estimation and noise distribution characteristic estimation are performed to generate distinguishing features. Utilizing the fact that image regions from different sources tend to have larger inter-class difference, the fuzzy c-means clustering is used to identify spliced regions. Compared to existing noise-based image splicing detection methods, experimental results on different datasets have shown that the proposed method has superior performance, especially when the noise difference between the spliced region and the original region is small. Moreover, the proposed method is robust for content-preserving manipulations.