Digital Image Forensics: An Improved DenseNet Architecture for Forged Image Detection

Abstract

Images sent across internet platforms are frequently subject to modifications, including simple alterations, such as compression, scaling, and filtering, which can mask possible changes. These modifications significantly limit the usefulness of digital image forensics analysis methods. As a result, precise classification of authentic and forged images becomes critical. In this study, a system for augmented image forgery detection is provided. Previous research on identifying counterfeit images revealed unexpected outcomes when using conventional feature encoding techniques and machine learning classifiers. Deep neural networks have been also utilized in these efforts, however, the gradient vanishing problem was ignored. A DenseNet model was created to tackle limitations inherent in typical Convolutional Neural Networks (CNNs), such as gradient vanishing and unnecessary layer requirements. The proposed DenseNet model architecture, which is composed of densely connected layers, is designed for precise discrimination between genuine and altered images. A dataset of forged images was implemented to compare the proposed DenseNet model to state-of-the-art deep learning methods, and the results showed that it outperformed them. The recommended enhanced DenseNet model has the ability to detect modified images with an astonishing accuracy of 92.32%.