FauxFind: Deep Learning-Based Deepfake Detection System using Transfer Learning and CNN

The recent breakthrough in deepfake technology has resulted in concerns for the authenticity of visual information present on the internet. In response to this grave problem, we propose a novel approach for deepfake image detection through the MesoNet algorithm. MesoNet is a type of CNN that has demonstrated its effectiveness in detecting faint manifestations of deepfake manipulations. In MesoNet, the mechanism of transfer learning is adopted to fine-tune a pre-trained model over a well-curated dataset consisting of real and deepfake images and thus learn very subtle patterns characteristic of artificial alterations. The performance of the proposed technique was rigorously tested on a range of datasets through extensive testing to show improved accuracy in identifying deepfake images with reduced false positives. In summary, the research findings indicate that the MesoNet algorithm could be used in detecting deepfake photos, serving as a powerful tool for preventing the spread of misinformation and preserving the integrity of visual content on digital platforms.