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meetpateltech

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Bjorkbat

Something I find weird about AI image generation models is that even though they no longer produce weird "artifacts" that give away that the fact that it was AI generated, you can still recognize that it's AI due to stylistic choices.

Not all examples they gave were like this. The example they gave of the word "Typography" would have fooled me as human-made. The infographics stood out though. I would have immediately noticed that the String of Turtles infographic was AI generated because of the stylistic choices. Same for the guide on how to make chai. I would be "suspicious" of the example they gave of the weather forecast but wouldn't immediately flag at as AI generated.

Similar note, earlier I was able to tell if something was AI generated right off the bat by noticing that it had a "Deviant Art" quality to it. My immediate guess is that certain sources of training data are over-represented.

mlsu

2 days ago

We are just very sharp when it comes to seeing small differences in images.

I'm reminded of when the air force decided to create a pilot seat that worked for everyone. They took the average body dimensions of all their recruits and designed a seat to fit the average. It turned out, the seat fit none of their recruits. [1]

I think AI image generation is a lot like this. When you train on all images, you get to this weird sort of average space. AI images look like that, and we recognize it immediately. You can prompt or fine tune image models to get away from this, though -- the features are there it's a matter of getting them out. Lots of people trying stuff like this: https://www.reddit.com/r/StableDiffusion/comments/1euqwhr/re..., the results are nearly impossible to distinguish from real images.

[1] https://www.thestar.com/news/insight/when-u-s-air-force-disc...

snek_case

2 days ago

I think it's because they're all trained on the same data (everything they could possibly scrape from the open web). The models tend to learn some kind of distribution of what is most likely for a given prompt. It tends to produce things that are very average looking, very "likely", but as a result also predictable and unoriginal.

If you want something that looks original, you have to come up with a more original prompt. Or we have to find a way to train these models to sample things that are less likely from their distribution? Find a way to mathematically describe what it means to be original.

quitit

a day ago

We can also pick up hints on discordant production value. This is quite noticeable on websites such as Amazon/Alibaba/Etsy/Ebay/etc where there's a lot of scam listings that use AI images for cheap or basic items.

So even though the image shown doesn't present obvious flaws, the fact that the image is high quality is the tell-tale sign of being AI generated.

This also isn't something that can be easily fixed - even if we produce convincing low production value imagery using AI, then the scam listing doesn't achieve its goal because it looks like junky crap.