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