Deep Shield: A Federated Learning Approach to Combat Online Pirated Content Using Deep Fakes

Online piracy and the spread of deep false content have become major issues in the digital age. In order to combat the widespread distribution of unlicensed information, notably through the use of deep fake technologies, this article offers “Deep Shield,” a unique solution employing federated learning techniques. Deep Shield uses a network of decentralised nodes to spread the capabilities of distributed machine learning while protecting user data privacy. We use federated learning to let several stakeholders, including platforms, regulatory authorities, and content providers, train a powerful deep learning model together without disclosing private information. By aggregating model changes and preserving data isolation, our technique ensures privacy compliance and reduces the risk of data breaches. Our research of the Deep Shield framework's design, training procedure, and deployment tactics is thorough. On a large collection of stolen content and deep fake films, we ran comprehensive trials to assess its efficacy. The findings show that Deep Shield considerably improves the identification and deletion of illegal information while upholding a high standard of privacy protection.