Video Authenticity Detection Using Web-Enabled Techniques

In light of the pervasive threat posed by deepfake videos in the digital realm, this study delves into the expansion of an innovative deepfake detection system. Leveraging state-of-the-art AI methodologies, including CNN’s, LSTM networks, and ResNext architectures, our research seeks to improve the precision and efficiency of deepfake identification. Departing from traditional manual inspection approaches, our suggested system integrates automated, real-time monitoring functionalities to swiftly identify and flag suspicious content. Through an exhaustive review of existing literature and methodologies, we identify key gaps and challenges in current detection methods, offering novel solutions to address them. This study contributes a unique combatting deepfake proliferation.