That's what I was saying.
The image gets processed by a separate image encoder (or vision model), which turns it into a vector embedding. The LLM itself never sees the raw pixels - it only works with that embedding. So when it judges whether an image is real or AI-generated, it's reasoning based on a compressed representation, not by directly "looking" at the picture.
From your link:
There are different encoders for each modality type, such as text encoder or audio encoder.
Each modality encoder is designed to extract specific information from its corresponding input data. For example, the text encoder might extract semantic representations from text, while the image encoder extracts visual features from images.
So the language model that's answering back to you never saw the image - the image encoder did and it then described it back to the language model.