The proposed correlation distillation approach can be extended to handle more diverse modalities beyond audio and visual by incorporating multi-modal fusion techniques. One way to achieve this is by integrating additional modalities such as text or sensor data into the framework. This can be done by creating separate branches within the model dedicated to processing each modality's features. For text data, natural language processing (NLP) models can be utilized to extract semantic information, while sensor data can provide contextual cues for detecting anomalies or inconsistencies in the data.
By incorporating these diverse modalities, the model can learn to capture correlations not only between audio and visual data but also between text, sensor data, and other modalities. This multi-modal approach can enhance the model's ability to detect deepfakes across a wider range of scenarios and forge a more comprehensive understanding of the data.