A Collaborative Deepfake Detection Framework Using Federated Learning for Enhanced Privacy and Security

Deepfake technologies have advanced rapidly in recent years, posing significant challenges for multimedia authentication and threatening privacy and security across digital platforms.Current detection methods often rely on centralized data storage and processing, which raises concerns about data privacy, especially when handling sensitive user information.To address these issues, this research proposes a collaborative deepfake detection framework based on federated learning designed to detect and mitigate deepfake media while preserving data privacy.The primary objective of this study is to develop an efficient, decentralized deepfake detection model that leverages the strengths of federated learning to create a shared, privacy-preserving approach to media verification.This framework enables multiple devices to collaboratively train a global deepfake detection model without sharing raw data, thereby enhancing user privacy and reducing the risk of data exposure.The federated learning model aggregates locally trained models from distributed devices to create a robust, unified model recognizing diverse forms of deepfake media across varied environments.Key research issues addressed include data heterogeneity, communication efficiency, and the need for real-time detection accuracy.To tackle these challenges, the proposed framework incorporates a lightweight convolutional neural network (CNN) architecture optimized for edge devices, ensuring minimal latency and efficient processing.Extensive simulations will evaluate the framework's effectiveness, using large-scale deepfake datasets to benchmark detection accuracy, computational efficiency, and privacy preservation.