Abstract
Image tempering is one of the significant issues in the modern era. The use of powerful tools for image editing with advanced technology and its widespread on social media raised questions on data integrity. Currently, the protection of images is uncertain and a severe concern, mainly when it transfers over the Internet. Thus, it is essential to detect an anomaly in images through artificial intelligence techniques. The simple way of image forgery is called copy-move, where a part of an image is replicated in the same image to hide unwanted content of the image. However, image processing through handcrafted features usually looks for pattern concerns with duplicate content, limiting their employment for huge data classification. On the other side, deep learning approaches achieve promising results, but their performance depends on training data with fine-tuning of hyperparameters. Thus, we proposed a custom convolutional neural network (CNN) architecture with a pre-trained model ResNet101 through a transfer learning approach. For this purpose, both models are trained on five different datasets. In both cases, the impact of the model is evaluated through accuracy, precision, recall, F-score and achieved the highest 98.4% accuracy using the Coverage dataset.