An image preprocessing tool to protect artworks from AI-for-Art based mimicry

These tools do not work in the real world for several reasons: first, people who train LoRA or models curate their datasets, removing adversarial noise is trivial: https://github.com/lllyasviel/AdverseCleaner ; second, if they are trying to defend themselves against training AI models, then it will probably do almost nothing at all, models trained from scratch will learn the distribution, even one where the samples have adversarial noise (which can only attack a frozen model, like a VAE), SDXL has a new VAE, so the VAE will just be more robust if there is adversarial noise on the images, because it will learn to ignore it, many other models do not have a VAE to begin with.

Also, resizing the image (as is almost always done when training the model) will probably destroy most if not all of the adversarial noise.

AI has open a real big can of worms.

Frankly, I see AI training on publicly available images to learn a style which it will then reproduce similar to the problem of game clones.

Imagine spending months/years making a game, coming up with the concept and gameplay, tuning the gameplay, … then after release cloners makes a nigh exact copy with slight changes in artwork/text and sell it for 10% of what you are selling - which they can since they put in only a fraction of the effort - and there is nothing you can do about it since gameplay isn’t copyrightable.

Same thing here IMHO. Not an AI specific problem but AI takes “no effort” when replicating an artist’s style vs a human who at least has to practice and learn the style.

This is incredibly similar to a paper one of my professors was working on a year or two ago: [0]

> Neural Trojans embedded in pre-trained neural networks are a harmful attack against the DNN model supply chain. They generate false outputs when certain stealthy triggers appear in the inputs. While data-poisoning attacks have been well studied in the literature, code-poisoning and model-poisoning backdoors only start to attract attention until recently.

While I agree with other commenters here that it's hard to imagine it being widely implemented at the moment, I think it's only the beginning of this type of tech. In a few years, maybe this type of data poisoning could be as ubiquitous as SSL and as subtle as webp compression artifacting.

[0]: https://dl.acm.org/doi/abs/10.1145/3548606.3560678

Cat-and-mouse.

It won't be hard to train models to recognize and compensate for this and other permutations of it. Meanwhile you've uglified your art piece.

I don’t see artists replacing all the images on their websites with Mist processed versions.

Their example Van Gogh images look okay as thumbnails but are really different from the originals when viewed larger.

Overall a neat project though.

If it's anything like Glaze, it's a complete meme. That took a lot of compute to glaze an image (with very visible artifacts) and was bypassed with some trival image processing on a shitty laptop.

this only works for publicly released model weights, right?

can it work for closed models, like midjourney?

Guess what. If an AI uses my work i'll sue. Simple as that.