A Unified Deep Learning Framework for Lip Reading and Deep Fake Audio Classification

This study proposes a multimodal framework designed to enhance deepfake detection by integrating visual lip-reading analysis and audio signal processing for real-time applications. Leveraging LipNet—a deep learning model pre-trained on the GRID dataset—the system employs 3D convolutional neural networks (CNNs) combined with bidirectional long short-term memory (Bi-LSTM) layers to interpret lip movements and decode spoken words from visual inputs. Simultaneously, audio analysis modules evaluate acoustic features to detect synthetic or manipulated speech. By independently training these components and combining their outputs, the framework identifies inconsistencies between visual and auditory modalities, such as mismatched lip-audio synchronization or unnatural speech patterns. Experimental results demonstrate robust performance in both word-level lip-reading accuracy and deepfake detection reliability, achieving high precision in flagging fraudulent content. The system’s effectiveness in detecting audiovisual anomalies positions it as a practical tool for digital forensics and security applications, with potential to foster trust in digital media. Future work will expand the model’s adaptability to diverse datasets, including multilingual contexts, and refine its integration into real-world media authentication and accessibility tools.