Performance Evaluation of Local Binary Patterns (LBP) for Copy-Move Forgery Detection in Digital Images: A Comparative Study

International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VIII, Issue IV, April 2023|ISSN 2454-6194

Performance Evaluation of Local Binary Patterns (LBP) for Copy-Move Forgery Detection in Digital Images: A Comparative Study

Hlaing Htake Khaung Tin
Faculty of Information Science, University of Information Technology, Myanmar

DOI: https://doi.org/10.51584/IJRIAS.2023.8421

Received: 30 March 2023; Accepted: 08 April 2023; Published: 09 May 2023

Abstract: Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

Keywords: Performance Evaluation, Local Binary Patterns (LBP), Copy-Move Forgery Detection, SIFT, SURF, Comparative Study.

I. Introduction

Copy-move forgery is a type of image forgery where a portion of an image is duplicated and pasted onto another location within the same image to conceal or tamper with certain details. This type of forgery is common in digital images and can be used to manipulate important information such as timestamps, faces, and text. Copy-move forgery detection is a critical task in digital image forensics, and several techniques have been proposed to detect this type of forgery.

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