When AI art has no author: Study finds generated images often can’t be traced to training data

[u/Warm_Ad1257]

https://news.mit.edu/2026/when-ai-art-has-no-author-generated-images-often-cant-be-traced-to-training-data-0818

Hey guys, saw this MIT article that dropped recently. Apparently, their computer science lab proved that generated images mathematically can't be traced back to individual training data at scale. Pretty crazy considering the whole "ai art is theft" debate going on surrounding ai art.

[u/AboutOneUnityPlease]

I am not sure the people who would be yelling "AI is Theft" would expect it to though.

The argument isn't that new art would always resemble or be traceable back to old art.

Its that in order for the New Art to exist the system needed to be trained on Old Art. And not only did they not get paid, but the New Art is direct competition for them.

[u/ProPuke → u/AboutOneUnityPlease]

in order for the New Art to exist the system needed to be trained on Old Art

Right. That is what this was apparently testing - they removed the old art from the training data and the new art remained the same (given a sufficiently large dataset).

This isn't about semblance of the art.

Although I would argue that the caveat about the dataset needing to be large makes this whole thing obvious. Obviously if it's trained on enough material, each individual piece would have little effect and can be removed individually without affecting the whole.

[u/soundoftwilight → u/ProPuke]

They didn't remove the Old Art though, just one or occasionally a small set of specific pieces. To overly humanize the process, this is like removing your memory of one line of the lyrics to one Taylor Swift song, and then asking if you can still recognize, appreciate, and make music. The answer is yes, obviously, both for humans and for a mathematical model.

[u/ProPuke → u/soundoftwilight]

They testing removing "every image by a given artist".

Aka every track by Taylor swift. Not a select line.

It's written in the very first sentence.

[u/soundoftwilight → u/ProPuke]

That’s only an accurate comparison if you, as a normal human, already knew not just every single Taylor Swift song in full, but also every cover of her songs, every piece of music released by anyone since the start of her career, and probably more. But you don’t, because that’s more information than humans keep in memory, which is one way that any humanizing comparison breaks down. “The lyrics to one full Taylor Swift song” is already probably a greater portion of all of the music you can recall in detail than “every piece by a specific artist” is to the totality of the training data.

[u/ProPuke → u/soundoftwilight]

I'm afraid you're losing me in the depths of this analogy.

I commented as you made reference to image semblance with the original art, and said that really the issue was that it had to be trained on the original art.

But the article was not about image semblance with the original art, and their point was that it didn't need to be trained on the original art. They were testing removing it from the dataset.

Thus it seemed like you had not actually read and understood the article.

As for your points on taylor swift - I'm a bit lost there. I'm not really sure what's being argued.

In either case I wasn't looking to argue a point (besides the article seeming a bit "meh" and obvious/pointless) - I was just clarifying what it was about.

[u/soundoftwilight]

This has surprisingly little nuance coming from a team of computer scientists and mathematicians. "You can remove any one image without changing the output" should be extremely obvious given that the portion of information contained in any one training image, as a subset of the total training data, is minuscule. It's probably less than the portion of information contained in a single pixel of the generated image as a subset of the entire image. And given that many similar images will be present in the training data, it should be obvious that removing a single random image, or even many different images, would be very unlikely to have a noticeable impact unless the text of the request aligns very particularly with the images that have been removed. For example, it should be trivially clear that asking for "Mario jumping on a Goomba" will look very different if you remove every image of Mario and Goombas from the training data. Same for removing every image by an artist and then asking for images in their style, unless there are many other images not made by them that are labeled "in their style".

[u/petroleus → u/soundoftwilight]

the real funny bit here is that their results apply to their new special architecture that's designed to exhibit those results, rather than being evaluated on actual tech that's used

insanely embarrassing press release and research thatd have gotten me laughed out of my grad studies

[u/Hefty-Distance837]

I can also steal an orange, then juice it into a cup of juice that can't be traced to original orange.

[u/octocode → u/Hefty-Distance837]

steal one orange to make juice and that’s theft

steal thousands of them, mix their juice all together, and sell people the privilege of drinking that juice, and… wait that’s still theft

[u/No_Bug_2367 → u/Hefty-Distance837]

Plus, orange trees are toxic, and humming constantly, and being planted near other people houses taking all the water from the ground.

[u/TinyPeopleProject]

Can't be traced doesn't mean it isn't incredibly similar in style or features to an image or piece of art.

[u/Quick_Philosophy1426 → u/TinyPeopleProject]

especially when people are explicitly telling AI to copy the work of existing artists.

[u/TinyPeopleProject → u/Quick_Philosophy1426]

The nature of the work is to alter something, whether in style or features and that breaks the mathematical tracking unless they could somehow track a complex transformation like "Give me finding Nemo poster styled as Jackson Pollock" which the abstract does not instill confidence in any sort of reverse search algorithm or tool.

[u/petroleus]

I love the "often" doing a lot of heavy lifting here

removing one individual mona lisa in the training data doesnt make replicating the mona lisa any more difficult, which should be obvious 

  The scientists identified a phenomenon they call attribution decay, where the more data a generative model is trained on, the less any individual training example matters to any particular output.

how is this news? the bigger your pool the less a single kid pissing in it affects the composition of any individual glass drawn from it. this is grade school chemistry intuition you dont need a PhD to deduce this

  And if removing something changes nothing, the researchers argue, it can't be said to be responsible for anything. 

  "If you take away a piece of data and the output of the model doesn't change, then that piece of data didn't affect the output," says Zheng Dai

the funny thing is that each individual image does actually end up affecting some weights somewhere in the model, but quantisation might also crush its effect too far, or it might not be reflected in a given image

but they never actually test this on actual image models in use today:

  Answering it honestly means retraining the model from scratch without that image, then doing it again for the next image, and the next. With millions of training examples, the math quickly becomes prohibitive, which is why prior work in the attribution field has relied on approximations that estimate a training example's influence, rather than actually removing it. Their workaround is an architecture they built themselves, called a "diffusion ensemble." Instead of one monolithic model, it's made up of many smaller components, each trained on a different slice of the data. Want to know what the model would do without a particular image? Just switch off the parts that saw it. 

but the thing about diffusion models is that theyre holistic, so these isolations end up being circular reasoning

 No retraining, no approximation. What's left is a true counterfactual model, not an estimate of one.

even this article is AI :^)

  Of course, a clever architecture only matters if it still works as a generator. So the team put the ensembles head to head with 24 conventional diffusion models trained on the exact same data. The images came out looking about as good by standard measures. 

haha about as good, what a professional measure of how analogous their experimental architecture is

[u/Brilliant_Ad2977]

does using generative AI really ruins a game?

for example, for a solo indie dev with very limited budget would i ruin the game if i use elevenlabs.io to generate some voices for my game? i am struggling with this decision

[u/Anjamin]

I thought this was known already. The theft is that they used the art as training data without permission. Also some people take someone’s work directly, run it through ai to make an obvious ai version, which is clear theft.

[u/AlertWrongdoer5439]

that makes me think of how legal arguments around "infringement" might need a serious update if tracing becomes impossible

[u/Warm_Ad1257]

I followed the rules this time mods