Beyond audio-visual inconsistencies, a similar approach can be applied to detect various other types of multimedia content manipulation. One potential application is the detection of deepfake videos, where facial expressions and lip movements in the visual modality may not align with the corresponding audio content. By training the model to recognize discrepancies between facial features, gestures, and spoken words, it can identify instances of deepfake manipulation. Moreover, the method can be extended to detect tampering in text overlays or subtitles within videos, ensuring that the textual information matches the audio and visual context accurately. Additionally, the approach can be adapted to identify anomalies in metadata, timestamps, or geolocation tags associated with multimedia content, providing a comprehensive verification system for detecting a wide range of manipulations across different modalities.