I hadn't been exposed to the new GPT-Image-2.5 model before this, so didn't have any "tells" to go off of, and found it almost impossible to begin with, but it seems the brain is pretty good at adapting for visual stuff! After 2 rounds, I was able to build up a 16-image combo, answering most within a second or two.

I think the "tell" I ended up with is that almost all of the AI images tend to center around a very obvious main "subject" (a flower, a bike, a wrench, etc) – presumably an inherent artefact of generating from a prompt – with everything else around the subject having a very strange depth to it. The depth, focus, and bokeh around the main subject just never look quite right. If you look at an image and find it has a very strong central subject with a little too much depth separation than expected, there's a good chance it's AI.

I recognise that this method of finding a consistent "tell" will probably not hold for future models, unfortunately. Everything else looks almost perfect at this point.

There are occasionally more obvious tells as well, if you look closely. Some examples that I saw:

- A boat cast a shadow, but the mooring rope didn't

- A bench leg was out of perspective, and didn't touch the ground

- In a pile of logs,the ends were sharpened in an unnatural way[0]

- In several there seems to be an inconsistent contrast between something that looks more dirty, worn, weathered, etc. while the surroundings are more clean and smooth.

Some also feel off in a way that I can't really articulate.

[0]: I think the relationship to the prompt was interesting here, the prompt actually asked for a stack of wooden fence posts, but they were really thick for fence posts, and looked more like firewood with a sharpened point. Interestingly, the prompt included the phrase "without excessive sharpening" which I think meant increasing the sharpness of the image, but I wonder if the AI interpreted as referring to the sharpness of the fence posts.

It's hard to put into words but the "texture" of GPT-Image-2.5 is just a bit off, like the difference between blue noise and white noise. It's like someone turned up the local contrast just a little too high.