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

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