Block-Based Forgery Detection: Performance Comparison Using HOG, LBP, and MBF

One of the types of forgery performed on digital images is copy and paste forgery (CPS). This type of forgery is realized by pasting another region copied from the same image over the relevant region of the image. It is very important to determine whether there is any forgery on these images, which can be used as evidence in many fields. In this study, an analysis on forgery detection is performed using HOG (Histogram of Oriented Gradients), LBP (Local Binary Patterns), and Multiscale Basic Features (MBF) features for block-based copy-paste forgery detection. The performance of various features alone and in combination is evaluated. Combinations such as HOG+LBP, HOG+MBF and MBF+LBP were tried, but the expected performance improvement was not achieved. Although the performance increase is not very high, the highest results are generally obtained with the LBP+MBF hybrid feature This approach resulted in an F1 score of 88.5%. This study contributes to existing methods in the field of block-based forgery detection and demonstrates the effectiveness of various feature combinations. In addition, although HOG and LBP features are frequently used in block-based approaches, approaches using the MBF feature have not been found in the literature. This study contributes to the existing methods in the field of block-based forgery detection and shows the effectiveness of various features and feature combinations.