Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions / / edited by Abhishek Kumar, Dr. Priya Batta, T. Ananth Kumar, S. Oswalt Manoj

• Autore: Kumar Abhishek
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2026
• Edizione: 1st ed. 2026.
• Descrizione fisica: 1 online resource (303 pages)
• Disciplina: 005.824; 005.74
• Soggetto topico: Blockchains (Databases); Security systems; Artificial intelligence; Data mining; Image processing; Machine learning; Blockchain; Security Science and Technology; Artificial Intelligence; Data Mining and Knowledge Discovery; Image Processing; Machine Learning
• Nota di contenuto: Introduction -- DeepFex: Detecting Deepfakes in the Digital Age -- Blockchain Technology Overview -- Federated Learning Basics -- Integrating Blockchain with Federated Learning -- Privacy and Security in DeepFex Solutions -- Real-World Applications and Use Cases -- Challenges, Ethics, and Regulatory Perspectives -- Future Directions -- Conclusion.
• Sommario/riassunto: This book highlights the transformative synergy between Blockchain and Federated Learning in developing privacy-focused solutions for DeepFex. By leveraging the decentralized nature of blockchain alongside the privacy-preserving capabilities of federated learning, it offers a novel approach to combating the growing challenges of deepfake technology. The integration of these two cutting-edge technologies ensures data security, model integrity, and transparent collaboration, making it possible to detect and mitigate deepfakes in a scalable, ethical, and decentralized manner.
• Titolo autorizzato: Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions
• ISBN: 981-9513-94-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911047707503321
• Lo trovi qui: Univ. Federico II
• Serie: Blockchain Technologies, . 2661-8346