Decentralized Deepfake Task Management Algorithm Based on Blockchain and Edge Computing

Abstract

Centralized deepfake service providers have large amounts of computing power and training data, giving them the ability to produce high-quality deepfake content. However, once these service providers are attacked or malfunction, it may lead to the collapse of the entire deepfake ecosystem, making deepfake a potential threat to data security. This monopoly development has led to the uneven distribution of deepfake resources, which in turn has brought about the risk of single points of failure. To deal with the problem, this paper proposes a decentralized deepfake task management algorithm (DD-TMA) based on blockchain and edge computing. The blockchain in this algorithm can provide a decentralized storage and management platform to ensure that the data and models of deepfake tasks will not be tampered with or lost. Edge computing can distribute tasks to edge devices close to the data source for processing, reducing data transmission delays and bandwidth consumption, and improving the efficiency and security of deepfake tasks. The paper innovatively integrates blockchain, federated computing, and edge computing. Firstly, the algorithm establishes a decentralized computing platform based on blockchain. Subsequently, it enhances computing power during the execution of decentralized deepfake tasks through the integration of federated computing and edge computing. Finally, the algorithm increases the active performers of decentralized deepfake tasks through gamification, thereby improving task execution efficiency. Experiments conducted in this study on public data sets demonstrate that the algorithm is efficient, robust, and reusable. Compared with other algorithms, the efficiency of DD-TMA is improved by more than 20% and the stability is improved by more than 13%. This algorithm proves effective in solving the problems encountered in the execution of centralized deepfake tasks. The research provides new ideas for future evaluations of decentralized deepfake effects based on different strategies.