DeepFakes, which refer to AI-generated media content, have become an
increasing concern due to their use as a means for disinformation. Detecting
DeepFakes is currently solved with programmed machine learning algorithms. In
this work, we investigate the capabilities of multimodal large language models
(LLMs) in DeepFake detection. We conducted qualitative and quantitative
experiments to demonstrate multimodal LLMs and show that they can expose
AI-generated images through careful experimental design and prompt engineering.
This is interesting, considering that LLMs are not inherently tailored for
media forensic tasks, and the process does not require programming. We discuss
the limitations of multimodal LLMs for these tasks and suggest possible
improvements.

通过定性和定量实验，我们调查了多模式大型语言模型在 DeepFake 检测中的能力，并展示了它们通过仔细的实验设计和及时的工程设计可以揭示 AI 生成的图像，而这一过程并不需要编程。我们讨论了多模式大型语言模型在这些任务中的局限性，并提出可能的改进。