Real-time and secure identity authentication transmission mechanism for artificial intelligence generated image content

The rapid development of generative artificial intelligence technology and large-scale pre-training models has led to the emergence of artificial intelligence generated image content (AIGIC) as an important application of natural language processing models. This has resulted in a significant shift and advancement in the way image content is created. As AIGIC requires the acquisition of substantial image datasets from user devices for training purposes, the data transmission link is highly complex, and the datasets are susceptible to illegal attacks from multiple parties during transmission, which has a detrimental impact on the integrity and real-time nature of the training data and affects the accuracy of the training results of the AIGIC model. Consequently, this paper proposed a real-time authentication mechanism to guarantee the secure transmission of AIGIC image datasets. The mechanism achieves anonymous identity protection for the user device providing the image dataset by introducing a certificate-less encryption system. In turn, an aggregated signature scheme with key negotiation algorithm is introduced to authenticate the user devices of legitimate image datasets. A performance analysis indicates that the mechanism proposed in this paper outperforms other related methods in terms of security and accuracy of AIGIC image model training results, while guaranteeing real-time transmission of AIGIC image datasets, at the same time, the time complexity is also lower, which can effectively ensure the timeliness of the algorithm.