Enhanced copy-move forgery detection using deep convolutional neural network (DCNN) employing the ResNet-101 transfer learning model

The rapid proliferation of high-quality false images on social media sites calls for research on legitimate image recognition systems. Copy-move forgery (CMF), which involves copying portions of an image, is one of the most commonly used image altering methods. Due to the problem of exploding and vanishing gradients, the present Convolutional Neural Network (CNN) model must be trained for up to 100 epochs to achieve the greatest accuracy. In this work, a deep CNN (DCNN) model using the residual network with 101 deep layers has been used. In order to solve the problem of exploding and disappearing gradients, the concept of skip connections has been included in the residual network. In addition, in order to maximize the performance of the suggested ResNet-101 model, the cyclical learning rate (CLR) hyper-parameter is utilized to further tune the model. The model was trained and evaluated using a variety of datasets, including MICC-F600, MICC-F2000, MICC-F220, and CoMoFoD v2. Accuracy, error rate, true positive rate (TPR), false positive rate (FPR), true negative rate (TNR), and false negative rate (FNR) were analyzed quantitatively. The proposed model achieves highest accuracy of 97.75% only after training the model for 5 epochs only for CoMoFoD v2 dataset. For MICC-F220, MICC-F600 and MICC-F2000 datasets the achieved accuracy was 96.09%, 97.63% and 96.87% respectively only after training the model up to 10 epochs. In order to demonstrate the efficacy of the suggested approach, a comparative study with various state-of-the-art-models available in the literature has been presented.