文生视频大模型设计的安全风险及其矫治

Security Risks and Remediation of the Design of Text-to-Video Generative Models

摘要: 本文深入探讨了文生视频大模型设计中的安全风险及其矫治策略。随着人工智能技术的快速发展，文生视频大模型如Sora和PixelDance等，已经能够根据文本描述生成视频内容，为影视、广告、教育等行业带来了革命性的变化。然而，这些技术进步也伴随着隐私泄露、数据安全、道德价值偏离等安全风险。本文分析了训练数据、提示词注入攻击、电信欺诈、道德价值偏离和人机交互等方面的风险，并介绍了差分隐私和联邦学习等风险治理策略。

Abstract: This article delves into the safety risks and rectification strategies in the design of text-to-video generative models. With the rapid advancement of AI technology, models such as Sora and PixelDance can generate video content based on textual descriptions, revolutionizing industries like film, advertising, and education. However, these technological leaps also come with safety risks, including privacy breaches, data security, and moral value deviations. The article analyzes risks in training data, prompt injection attacks, telecommunication fraud, moral value shifts, and human-computer interaction, and proposes risk governance strategies like differential privacy and federated learning to ensure the healthy development of technology and the harmonious stability of society.