Deep Learning-Based Digital Image Forgery Detection System

The advancements of technology in every aspect of the current age are leading to the misuse of data. Researchers, therefore, face the challenging task of identifying these manipulated forms of data and distinguishing the real data from the manipulated. Splicing is one of the most common techniques used for digital image tampering; a selected area copied from the same or another image is pasted in an image. Image forgery detection is considered a reliable way to verify the authenticity of digital images. In this study, we proposed an approach based on the state-of-the-art deep learning architecture of ResNet50v2. The proposed model takes image batches as input and utilizes the weights of a YOLO convolutional neural network (CNN) by using the architecture of ResNet50v2. In this study, we used the CASIAᵥ1 and CASIAᵥ2 benchmark datasets, which contain two distinct categories, original and forgery, to detect image splicing. We used 80% of the data for the training and the remaining 20% for testing purposes. We also performed a comparative analysis between existing approaches and our proposed system. We evaluated the performance of our technique with the CASIAᵥ1 and CASIAᵥ2 datasets. Since the CASIAᵥ2 dataset is more comprehensive compared to the CASIAᵥ1 dataset, we obtained 99. 3% accuracy for the fine-tuned model using transfer learning and 81% accuracy without transfer learning with the CASIAᵥ2 dataset. The results show the superiority of the proposed system.