Image Forgery Detection using ResNet50

Abstract

Abstract: Image forgery detection is crucial in ensuring the integrity of digital media. In this study, we propose a method for detecting image tampering using Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs) with a ResNet50 architecture. Leveraging the CASIA 2.0 Image Tampering Detection Dataset, which consists of authentic (Au) and tampered (Tp) images, along with metadata and annotations provided in the CASIA 2 Ground truth dataset, we develop and evaluate our model. The dataset comprises 7492 authentic images, 5125 tampered images, and 5123 files of ground truth information. ELA transformations highlight compression discrepancies, aiding in the identification of tampered regions. Our ResNet50-based CNN model, augmented with Global Average Pooling, Dense layers, and Dropout regularization, is trained using Adam optimization and binary cross-entropy loss with early stopping. Evaluation metrics, including training and validation loss/accuracy curves and confusion matrices, are used to assess model performance. The trained model is saved for future use and tested on new images to demonstrate its classification capabilities. Our approach achieves a significant level of accuracy in distinguishing between authentic and tampered images, underscoring its potential for practical image forgery detection applications and contributing to advancements in digital media forensics