This research introduces a novel approach to detect digital audio file forgeries using a cyclical system aided by mobile crowdsourcing. The study emphasizes the importance of metadata and file structure analysis in detecting container-based forgeries, leveraging the entrepreneurial spirit of innovation and market adaptability. The researchers developed a mobile web-based prototype system to collect diverse audio data and automatically detect forgeries, validated through scenario-based testing. The system's effectiveness is demonstrated in identifying forged audio files, with a focus on practical validation and user-friendly design. The collected dataset, which includes audio files from various smartphone models and recording apps, will be publicly available, serving as a valuable resource for future forensic investigations. The research highlights the need for scalable and adaptable solutions in digital forensics, fostering collaboration and knowledge sharing in the academic and research communities.This research introduces a novel approach to detect digital audio file forgeries using a cyclical system aided by mobile crowdsourcing. The study emphasizes the importance of metadata and file structure analysis in detecting container-based forgeries, leveraging the entrepreneurial spirit of innovation and market adaptability. The researchers developed a mobile web-based prototype system to collect diverse audio data and automatically detect forgeries, validated through scenario-based testing. The system's effectiveness is demonstrated in identifying forged audio files, with a focus on practical validation and user-friendly design. The collected dataset, which includes audio files from various smartphone models and recording apps, will be publicly available, serving as a valuable resource for future forensic investigations. The research highlights the need for scalable and adaptable solutions in digital forensics, fostering collaboration and knowledge sharing in the academic and research communities.