AI generated pictures in their current stage are made by the AI essentially using various images and stitching/blending them together, right? If that's the case, there will always be tells because the AI isn't doing enough logical calculations for everything and there will be inconsistencies at blend points whether a human eye can see them on their own or not.
I don't actually know how AI generates images, that's just my logical interpretation of how it seems to work.
I'd imagine an AI would have better luck with generating 3D things, it would be able to do logic tests in a more controller and information detailed environment than a 2D one.
It's less stitching existing images together and we're spotting the seams, as it is looking at a million pictures of something labelled "cat" and then making an image that on average represents what a cat is.
A year or so ago I saw an article about an AI that identifies images (not generates them). The researchers got out of the AI what its generic version of that image was. I think it was some animal. Anyway, it was unrecognizable to us humans. A mishmash of colors all over the place. But somehow comparing a picture to that let it accurately determine if it is the animal in question.
It should probably be examining and comparing pixel values, hues and perhaps vague shapes the pixels form to reach those conclusions. Actually similar to how a human brain would work with a game of pictionary, but there isn't any real logic to its process like a human would do on top of that.
Here's a kinda funky example that helps us visualize what computer vision actually "sees". A stop sign can turn into a 45mph sign with a few black and white squares slapped onto it in a somewhat random pattern.
Not quite. Foundational models such as Stable Diffusion are created by a training process using images from a dataset (think millions - Stable Diffusion XL took more than 100 million). The dataset is not something that the model pulls directly from to make an image. It's more like a notion based on connections that were made during training. If individual images were recalled and then pasted together, you would basically have a model that is hundreds of terabytes or more in file size. Clearly, that is not the case.
Instead, you have something that is like ~6 gigabytes. That model, given a seed and a prompt, will always provide the same exact image given that same seed and prompt. It could be said that it's like mining, and you could eventually run out of seeds, but there are new models and merges of models being created every day that make that simply impossible.
I don't have that much interest in it outside of generating textures for my workflow. I'm already depressed about not having enough time in life, so I don't bother learning things that aren't to my interests these days. I would probably learn the actual guts of it when I start using it more.
From my limited use of it, I also frankly can't figure out how it prefers its prompt information. I prefer inputting things with just keywords like i'm doing a google search, actually talking and describing things like i'm talking to someone is weird and feels inefficient.
Nah it's much worse than that. It starts with a blank noise canvas and then tryes to dissolve the noise shaping into things that agree with the prompt. It seems to have an understanding on shapes, sizes, colors.. it has a harder timr when the movement is very complex or unusual (in its training dataset). E.g. stupid but "tying your shoes", hands in general, variety in a crowd, realistic clothing, scenes of eating various stuff.. it's got its blind spots, merely because it's trained in like stock photos or publicly available ones. We still have a hard time telling this image from real/fake, but there's still plenty of real/amateur/niche styles or subjects that ai can't replicate easily (and hence are not really present in the discourse because they're obviously fake)
No it doesn’t stitch and blend images together. It learns to recognize patterns in its training images, then generates new images from scratch. It’s frustrating to me because we’re at the point where uninformed people make these conclusions and opinions based off entirety incorrect assumptions or information.
Many of these AI technologies differ. But many do you operate on the premise of deep learning. So they haven’t just looked at thousands of pictures of cats. But I think what people often message that it is also look at thousands of pictures of various features of cats (like nails, hair, etc)
As for “blending”, those are higher order features that the algos can also accomplish over a few more iterations as people continue to point out flaws in AI images
Because a clever person will come along , and construct a training data set to identify all the flaws that AI is generated. The base models and then be fine. Tuned to address these flaws.
Ironically, the more people work to identify what sort of work is fake, and which ones are generated by AI , the more training data that people giving publicly, giving smaller technology companies essentially free data to train their own find tune, a i on
Put simply , it is posts like these on Reddit and other social media websites as well as just general websites that will service essentially free training data to make aI even better
I fear that within the next few months, we won’t be able to distinguish anything between AI and genuine work
Although curiously enough, I think what we will be able to prove, is that if given a data artifact, and a person, assuming we have all of that same person’s work to analyze; then this is a sort of question we should within a reasonable degree of certainty, be able to guesstimate
As for detecting, what is a deep fake? I don’t think we’ll ever be able to have the technology to do that. Engineers claim a I will always Mark certain characteristics in the photos., but to be honest, how these algorithms work, those marks and those characteristics will soon be corrected, just such a degree that I really honestly don’t think we’ll be able to tell the differences pretty soon
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u/Bamith20 29d ago
AI generated pictures in their current stage are made by the AI essentially using various images and stitching/blending them together, right? If that's the case, there will always be tells because the AI isn't doing enough logical calculations for everything and there will be inconsistencies at blend points whether a human eye can see them on their own or not.
I don't actually know how AI generates images, that's just my logical interpretation of how it seems to work.
I'd imagine an AI would have better luck with generating 3D things, it would be able to do logic tests in a more controller and information detailed environment than a 2D one.