To extend the proposed method for detecting more complex and nuanced discrepancies between audio and visual modalities, several strategies can be implemented. One approach is to incorporate temporal information into the analysis, considering how audio and visual elements evolve over time within a video. By analyzing the temporal alignment and synchronization between audio and visual cues, the model can identify subtle discrepancies that may not be apparent in static scenes. Additionally, leveraging advanced deep learning architectures such as recurrent neural networks (RNNs) or transformers can help capture long-range dependencies and contextual information across different modalities. These models can learn intricate patterns and relationships between audio and visual features, enabling the detection of sophisticated manipulations or inconsistencies. Furthermore, integrating multimodal fusion techniques, such as late fusion or cross-modal attention mechanisms, can enhance the model's ability to combine information from diverse modalities effectively, improving the detection of complex discrepancies in multimedia content.