The emergence of deep networks has enabled substantial advancements in the pasture of sensory technology. The proliferation of visual media, coupled with the availability of advanced editing software, has led to a considerable enlarge in the ability to modify digital content. The user’s text is already academic in nature. No further rewriting is necessary. In order to detect and recognize fraudulent actions, we have put forth innovative methodologies. In this research, we discuss two fundamental aspects of employing deep convolutional neural networks in the context of image forgery detection. Firstly, an examination is conducted on different pre-processing techniques in combination with the Convolutional Neural Network (CNN) architecture. Following this, we evaluate the effectiveness of several transfers learning methods, including the utilization of pre-trained ImageNet models through fine-tuning. These techniques are applied to our dataset, CASIA V2.0. The focus of our study entails the examination of preprocessing techniques utilizing a straightforward convolutional neural network framework, while also exploring the profound impact of transfer learning models.