<![CDATA[The increasing of manipulated images, often shared on social media platforms, poses significant challenges for distinguishing authentic content from forgeries. This study aims to enhance the detection of tampered images by integrating Error Level Analysis (ELA) with Convolutional Neural Networks (CNNs). Specifically, the objectives are to evaluate the performance of two CNN architectures, VGG16 and MesoNet, combined with ELA preprocessing, and to identify potential avenues for future improvements in forgery detection. The dataset used comprises 7,492 authentic and 5,124 tampered images, sourced from the CASIA database, and is complemented with images from the Milborrow University of Cape Town (MUCT) dataset. Images were preprocessed using ELA to amplify discrepancies caused by tampering before being analyzed by the CNN models. The results indicate that the proposed ELA-VGG16 model achieved an accuracy of 86.786%, while the ELA-MesoNet model demonstrated superior performance, with an accuracy of 92.7%. These findings highlight the potential of combining ELA preprocessing with CNN architectures for robust image forgery detection. Despite fluctuations in training curves and instances of overfitting, the model effectively detects subtle manipulations in the majority of cases. However, challenges such as false positives and generalization to diverse datasets persist. Future research should explore enhancements such as expanded data augmentation, the integration of multi-model architectures,such as Xception or capsule networks, and advanced preprocessing techniques, which could further refine the model’s applicability and accuracy. These efforts would advance both the practical detection of forgeries and theoretical developments in informatics visualization, addressing critical challenges in digital forensics and media integrity.]]>
