LLM-Enhanced Deepfake Detection: Dense CNN and Multi-Modal Fusion Framework for Precise Multimedia Authentication

The increasing ubiquity of deepfake technology presents a serious risk to the legitimacy and reliability of multimedia material across a number of sectors, from identity verification to news distribution. Current deepfake detection methods frequently have trouble identifying minor manipulations and are unable to keep up with the latest generation techniques, which creates a serious vulnerability in the defence against the improper use of synthetic media. Using Dense Convolutional Neural Networks (Dense CNN) and Multi-Modal Fusion, this study presents a novel method for deepfake detection. Our Dense CNN architecture which draws inspiration from Dense Net improves sensitivity to complex manipulations while optimizing feature reuse through dense connectivity patterns, thereby mitigating the shortcomings of existing systems. Our suggested approach dynamically combines temporal and visual modalities, enhanced by Multi-Modal Fusion, to offer a comprehensive contextual knowledge that enhances detection accuracy. After thorough tests on several datasets, our method performs exceptionally well and is particularly good at identifying advanced deepfake variations. Our suggested methodology improves the state-of-the-art in deepfake identification by addressing the shortcomings of current systems and providing a reliable response to the urgent problems brought on by the malicious usage of synthetic media in practical applications. Adding Large Language Models (LLMs) is essential to improving the accuracy of the system. To add another level of scrutiny, LLMs are deliberately used to characterize and analyze portions of multimedia information that are vulnerable to manipulation.