Audio Visual Deepfake Detection

Audio-visual deepfake detection research aims to develop robust methods for identifying manipulated videos and audio where either or both modalities have been synthetically altered. Current efforts focus on detecting subtle inconsistencies between audio and visual streams using techniques like fine-grained feature analysis, contextual cross-modal attention, and statistical modeling of feature distributions, often employing recurrent neural networks or other deep learning architectures. These advancements are crucial for mitigating the spread of misinformation and enhancing the security of biometric authentication systems, impacting both the scientific community through the development of novel machine learning approaches and practical applications in media verification and security.