ENHANCING DEEPFAKE DETECTION: LEVERAGING MTCNN AND INCEPTION RESNET

Abstract

In response to the escalating threat posed by manipulated facial imagery in the digital age, our research project is dedicated to developing an advanced framework for the detection and classification of such content, augmented by a proactive user reporting mechanism. Leveraging state-of-the-art deep learning models like Multi-Task Cascaded Convolutional Networks (MTCNN) and InceptionResnetV1, our framework achieves an impressive accuracy rate of 92% in distinguishing between genuine and manipulated faces. The integration of explainability methods such as Grad-CAM enhances model interpretability, empowering users to understand model predictions. Additionally, our user-centric reporting interface enables active user participation in identifying and flagging potentially manipulated content, fostering transparency and accountability in digital media platforms. With the continued proliferation of deepfake technology, our research endeavors not only advance facial image analysis techniques but also uphold principles of trust and integrity in the digital realm, aiming to safeguard the credibility of information dissemination through vigilance, innovation, and collaborative action. Keywords: Deepfake detection, Facial image manipulation, Deep learning models, User reporting mechanism, Model interpretability, Transparency in digital media, Trust and integrity, Information dissemination, Vigilance, Collaborative action.