Smart Novel Computer-based Analytical Tool for Image Forgery Authentication

TL;DR

This paper introduces a new computer-based tool combining image forgery detection with facial recognition using BPNN, enhancing image authentication accuracy before recognition.

Contribution

It proposes a novel integration of forgery detection with facial recognition using BPNN, including a universal GUI tool with 2% error rate for improved image security.

Findings

Achieved 2% error rate in forgery detection.

Enhanced facial recognition reliability through pre-authentication.

Integrated tool improves overall image security process.

Abstract

This paper presents an integration of image forgery detection with image facial recognition using black propagation neural network (BPNN). We observed that facial image recognition by itself will always give a matching output or closest possible output image for every input image irrespective of the authenticity or otherwise not of the testing input image. Based on this, we are proposing the combination of the blind but powerful automation image forgery detection for entire input images for the BPNN recognition program. Hence, an input image must first be authenticated before being fed into the recognition program. Thus, an image security identification and authentication requirement, any image that fails the authentication/verification stage are not to be used as an input/test image. In addition, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of 2% error rate.