Design an Advance computer-aided tool for Image Authentication and Classification

TL;DR

This paper introduces a universal, intelligent image forgery detection and facial recognition tool that combines multiple image processing techniques and neural networks to improve accuracy and efficiency in verifying image authenticity.

Contribution

The paper presents a novel integrated smart tool with programmable architecture and dual databases for high-accuracy image forgery detection and facial recognition using BPNN.

Findings

Achieves 98% accuracy in forgery detection

Integrates multiple image processing techniques

Provides efficient automatic authentication

Abstract

Over the years, advancements in the fields of digital image processing and artificial intelligence have been applied in solving many real-life problems. This could be seen in facial image recognition for security systems, identity registrations. Hence a bottleneck of identity registration is image processing. These are carried out in form of image preprocessing, image region extraction by cropping, feature extraction using Principal Component Analysis (PCA) and image compression using Discrete Cosine Transform (DCT). Other processing includes filtering and histogram equalization using contrast stretching is performed while enhancing the image as part of the analytical tool. Hence, this research work presents a universal integration image forgery detection analysis tool with image facial recognition using Back Propagation Neural Network (BPNN) processor. The proposed designed tool is a multi-function smart tool with the novel architecture of programmable error goal and light intensity. Furthermore, its advance dual database increases the efficiency of a high-performance application. With the fact that, the facial image recognition will always, give a matching output or closest possible output image for every input image irrespective of the authenticity, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of 2% error rate. Meanwhile, a novel structure that provides efficient automatic image forgery detection for all input test images for the BPNN recognition is presented. Hence, an input image will be authenticated before being fed into the recognition tool.