Image Splicing Forgery Detection using Standard Division-Local Binary Pattern Features

Abstract

Numerous aspects of daily life contribute to societal stability, and the security of people's perceptions of the world online is one target of various malicious attacks. Professional forgers can now quickly create copy-move, splice, or retouch photos with the use of today's advanced tools. It has been determined that splicing, is a widespread method of manipulating images. Image forgery can also lead to substantial setbacks and challenges, some of which may have significant ethical, moral, or legal consequences. Thus, the paper proposes a system that combines SD-LBP (Standard Devision-Local Binary Pattern) based passive picture splicing detection system and ANN classifier. The SD-LBP is created to have the benefits and avoid drawbacks of Local Binary Pattern (LBP). The SD-LBP extraction is typically performed by employing proposed SD value-based thresholding instead of the center pixel, which is robust to noise and other photometric attacks. The second part of the proposed system is the ANN classifier is used that extract the feature of images to lower the error and build a model that can tell spliced images from real photos that have been digitally altered. The proposed system is creating a reliable image forgery detection technique that was implemented with CASIA V2.0 standard dataset. The results showing that it outperformed compared with other methods on the in terms of accuracy (97.8%), sensitivity (98.6%), and specificity (97.1%). Most importantly, the proposed SFD method exceeded the state-of-the-art efforts in this field in terms of accuracy.