Securing Visual Integrity: Machine learning approaches for forged image detection

Abstract

Image forgery detection is a critical area of digital forensics, attempting to discover manipulated regions within images to assure their authenticity and integrity. This study investigates the use of machine learning techniques, particularly the Convolutional Neural Networks for image fraud detection. The suggested method involves training classifier to distinguish between original and counterfeit images using extracted features or patches. An image dataset is divided into training and testing sets in this study to facilitate CNN training on patches corresponding to original images. The accuracy of the trained model in identifying phony regions is then evaluated using an additional test set. To measure the effectiveness of the CNN-based forgery detection system, evaluation criteria such as accuracy, precision and recall are used. Proposed system achieves 99.15% accuracy with VGG16 network with tuned parameters.