Authors - Vishalsinh Bais, Mansi Pagdhune, Wamik Khan, Gaurav Maske, Aditya Umredkar, Amol P. Bhagat Abstract - The rise of deepfakes has raised questions about the veracity of digital content. This has led to a lot of research into reliable detection techniques. In this study, we introduce a new deepfakes detection approach based on the mesoNet architecture and the use of convolutional neural networks (CNNs). The proposed model has a multi-layer structure that includes convolutional layers, pooling layers, and dropout techniques to effectively extract and discriminate features. Training on a dataset that includes our own forged images and deepfakes, this model shows promising results in identifying manipulated content. With the activation function of leaky ReLU, our mesoNet model shows great promise in accurately distinguishing deepfakes from real images. Our experimental results demonstrate its effectiveness in distinguishing between forged and real images, demonstrating its value as a powerful tool in the fight against digitally manipulated content.
