Automated Detection of Container-based Audio Forgery Using Mobile Crowdsourcing for Dataset Building

Abstract

This research paper introduces a new approach for detecting digital audio file forgeries, highlighting entrepreneurship spirit through innovation and market adaptability. It utilizes a cyclical system aided by mobile crowdsourcing to collect a comprehensive dataset of smartphone recordings, demonstrating an entrepreneurship approach to leveraging technology for scalable solutions. Digital forgery, an area ripe for entrepreneurship investment, is becoming increasingly accessible, making it challenging to detect manipulated audio files. This new method emphasizes the utility of metadata and file structure analysis and is scalable, reflecting an entrepreneurship mindset in creating adaptable and sustainable solutions. The researchers developed a mobile web-based prototype system to collect diverse audio data and automatically detect forgeries, showcasing their initiative and innovative thinking. They also conducted scenario-based testing to validate the effectiveness of their methodology, a step that underscores the entrepreneurship value of practical validation. This approach has the potential to significantly advance digital forensic practices by enabling broader detection of manipulated audio files, opening avenues for entrepreneurship ventures in digital security. The collected dataset will be made publicly available and serve as a valuable resource for future forensic investigations, encouraging entrepreneurship collaboration and knowledge sharing. The paper lays the groundwork for future research in expanding the scope of digital forensics, fostering technological innovation, and enhancing participatory models for data collection, all of which are essential elements in entrepreneurship ecosystems.