Integrating the proposed method into a comprehensive content verification system involves considering multiple modalities and types of manipulation to ensure robust detection capabilities. One approach is to develop a modular framework that incorporates specialized detectors for various types of manipulations, such as audio-visual inconsistencies, deepfakes, text tampering, and metadata alterations. Each detector can focus on a specific aspect of content verification, leveraging the strengths of the proposed method for audio-visual scene classification. By combining these detectors within a unified system, content authenticity can be assessed holistically, taking into account the interplay between different modalities and manipulation techniques. Furthermore, implementing a feedback loop mechanism that iteratively refines the detection algorithms based on new data and emerging manipulation trends can enhance the system's adaptability and effectiveness over time. By continuously updating and expanding the detection capabilities to address evolving threats in multimedia content manipulation, the comprehensive verification system can provide robust protection against disinformation and fraudulent media practices.