Splicing Image Forgery Detection and Localization Based on Color Edge Inconsistency using Statistical Dispersion Measures

Nowadays, due to the availability of low-cost and high-resolution digital cameras, and the rapid growth of user-friendly and advanced digital image processing tools, challenges for ensuring authenticity of digital images have been raised. Therefore, development of reliable image authenticity verification techniques has high importance in digital life. In this paper, we proposed a blind image splicing detection method based on color distribution in the neighborhood of edge pixels. First, we extracted edge pixels using contourlet transform. Then, to accurately distinguish the authentic edges from tampered ones, Interquartile Range (IQR) criteria are utilized to illustrate the distribution of Cr and Cr histograms of the spliced boundaries in YCbCr color space. Finally, a segmentation method is used to improve the localization performance and to reduce especially the computational time. The effectiveness of the method has been demonstrated by our experimental results obtained using the Columbia Image Splicing Detection Evaluation (CISED) dataset in terms of specificity and accuracy. It is observed that the proposed method outperforms some state-of-the-art methods. The detection accuracy is approximately 97 with 100% specificity.