  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.
