# Design of a Trusted Content Authorization Security Framework for Social Media
## 1 Introduction  
Since the official launch of the generative artificial intelligence model represented by ChatGPT (Chat Generative Pre-trained Transformer) in November 2022 [1], artificial intelligence has greatly contributed to our daily lives and promoted the rapid development of the digital economy. Traditional artificial intelligence (AI) mainly focuses on specific fields, such as data extraction and prediction. In contrast, generative AI models can produce new artificial intelligence-generated content (AIGC). However, this also raises security challenges in multiple respects, such as technology abuse represented by content security and user identity privacy. The Internet information space has long faced challenges of fake information and information content security [2]. Domestic and foreign social platforms [3],such as Facebook, Twitter, WeChat, Weibo, etc. [4], have continuously improved their governance ability for fake content and information security [5]. With the continuous growth in AIGC volume, the challenges of counterfeit information and information content security are also increasing. In recent years, the open source of domestic large language models has enabled the general public to train personalized large language models with less computing power, which has significantly reduced the threshold for using generative artificial intelligence. This technology is widely used in network platforms to generate short videos, articles, and AI graphics automatically. In today's society, short video software has increasingly begun to adopt generative artificial intelligence technology. These applications use productive artificial intelligence technology to create special effects, allowing users to easily add and edit high-quality effects, further enhancing the appeal of their short videos. However, this has brought much AIGC (artificially generated content) into social media and has spawned many problems [6], including forgery, false information dissemination, moral and ethical issues, excessive attention to inappropriate content, and infringement. Therefore, we need to face the challenges AIGC brings [7]. These problems go beyond the ability of traditional content risk control to address. Illegal actors can use open-source AIGC models or tools to generate a wide range of false information with lower thresholds and higher efficiency, infringing copyright [8] and quickly stealing user identities [9], representing new fraudulent and other illegal activities. In addition, since the training data of AIGC models mainly come from the Internet, the authenticity and accuracy of AI-generated information cannot be guaranteed. Privacy leakage issues have occurred in GPT-2, indicating that personal privacy data have been included in the model training dataset. In this era of digital information explosion, information authenticity, accuracy, and compliance have become urgent problems [10]. Effective control measures will make measuring the potential impact of AIGC on society, individuals, and social media easier. Therefore, this paper proposes an authorization security framework called \"spoken\", which includes a complete and adequate set of content information control strategies to ensure the security of user identity and the authenticity of the published content to reflect the user's original intention. This method provides a solid and reliable security solution for social media [11]. The contributions of this paper are as follows:  
1. This study considers a series of issues brought about by the rapid development of artificial intelligence-generated content (AIGC), focusing on managing published content in social media. The research seeks practical solutions to address these issues, protect personal rights, and maintain data privacy. 2. To ensure the uniqueness of user identity, this framework extracts the multimodal information users publish on social media. It integrates it with user identity information, timestamp, address label, and network IP address information into a new token.