[论文解读] Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models

Mohamed R. Shoaib, Zefan Wang+2 more|arXiv (Cornell University)|Nov 29, 2023

Misinformation and Its Impacts被引用 15

一句话总结

这篇论文在前沿AI与大规模生成模型时代对深度伪造、错误信息和虚假信息进行综述，提出一个综合防御框架，结合检测、认证、跨平台协作和政策措施。

ABSTRACT

With the advent of sophisticated artificial intelligence (AI) technologies, the proliferation of deepfakes and the spread of m/disinformation have emerged as formidable threats to the integrity of information ecosystems worldwide. This paper provides an overview of the current literature. Within the frontier AI's crucial application in developing defense mechanisms for detecting deepfakes, we highlight the mechanisms through which generative AI based on large models (LM-based GenAI) craft seemingly convincing yet fabricated contents. We explore the multifaceted implications of LM-based GenAI on society, politics, and individual privacy violations, underscoring the urgent need for robust defense strategies. To address these challenges, in this study, we introduce an integrated framework that combines advanced detection algorithms, cross-platform collaboration, and policy-driven initiatives to mitigate the risks associated with AI-Generated Content (AIGC). By leveraging multi-modal analysis, digital watermarking, and machine learning-based authentication techniques, we propose a defense mechanism adaptable to AI capabilities of ever-evolving nature. Furthermore, the paper advocates for a global consensus on the ethical usage of GenAI and implementing cyber-wellness educational programs to enhance public awareness and resilience against m/disinformation. Our findings suggest that a proactive and collaborative approach involving technological innovation and regulatory oversight is essential for safeguarding netizens while interacting with cyberspace against the insidious effects of deepfakes and GenAI-enabled m/disinformation campaigns.

研究动机与目标

• 解释前沿AI与基于LM的GenAI如何放大深度伪造与错误信息。
• 回顾现有的检测、认证和政策方法，以应对AI生成内容（AIGC）。
• 提出一个结合技术、协作、政策与教育的综合防御框架。
• 强调伦理、社会与治理影响，并呼吁多方利益相关者行动。

提出的方法

• 对深度伪造、错误信息与前沿AI的现有文献进行综合。
• 讨论基于LM的GenAI模型、训练与内容生成能力。
• 提出一个包含技术、战略、监管与教育组件的综合防御框架。

实验结果

研究问题

• RQ1 前沿AI与基于LM的GenAI如何影响深度伪造与错误/信息的产生和传播？
• RQ2 为了减轻AI生成内容威胁，需要哪些技术性、跨平台和政策驱动的防御机制？
• RQ3 指导GenAI与AIGC开发部署的伦理与社会考量应是什么？
• RQ4 多方利益相关者协作与教育如何提升对抗深度伪造与错误信息的韧性？

主要发现

• 检测算法和AI驱动的认证方法至关重要，但在与深度伪造生成的对抗中正演化的军备竞赛。
• 跨平台协作、透明报告和用户教育对于缓解AIGC的传播和影响至关重要。
• 结合技术、政策与教育的综合防御框架可以提升对深度伪造及错误/虚假信息的韧性。
• 学术界、产业界与政府之间的开放协作对于共享数据、标准和最佳实践是必要的。