Color Image Splicing Localization Based on Block Classification Using Transition Probability Matrix

Abstract

With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. Image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the stateof-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks.

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

“…This approach aims to group regions that exhibit similar features, which can indicate tampering or manipulation in the image. Yildirim et al [16] delineated the boundaries of spliced regions using an SVM and precisely localized them. Therefore, they applied Connected Component Labeling (CCL) to further refine and identify spliced regions.…”