DEEPFAKE DETECTION SYSTEM USING CODE, YOLO, AND MELODYMACHINE

Abstract

To detect manipulated multimedia content, the DeepFake Detection System utilizes advanced artificial intelligence methods. A more robust deepfake detection method is offered by the system using audio and video analysis. To identify faces and areas of interest, the video module utilizes "YOLO (You Only Look Once)". Next, it analyzes the data with "CODE (Contrastive Deepfake Embeddings)" to obtain high-dimensional features and find inconsistencies, like unusual textures and facial movements. To detect manipulation, MelodyMachine processes the audio by monitoring pitch shifts, tone, phoneme sequence, and temporal consistency. Through cross-modal analysis by comparing video and audio lip movement timing, the system can perform detection even better. An effective tool for identifying deepfakes, this pipeline's multiple steps ensure accurate classification of audio and video content. A reliable mechanism for authenticating audiovisual content has been established by the coupling of YOLO, CoDE, and MelodyMachine. This method allows for thorough detection