Comment on This new data poisoning tool lets artists fight back against generative AI
ayaya@lemdro.id 1 year ago
Obviously this is using some bug and/or weakness in the existing training process, so couldn’t they just patch the mechanism being exploited?
Or at the very least you could take a bunch of images, purposely poison them, and now you have a set of poisoned images and their non-poisoned counterparts allowing you to train another model to undo it.
Sure you’ve set up a speedbump but this is hardly a solution.
Obviously this is using some bug and/or weakness in the existing training process, so couldn’t they just patch the mechanism being exploited?
I'd assume the issue is that if someone tried to patch it out, it could legally be shown they were disregarding people's copyright.
It isn't against copyright to train models on published art.
The general argument legally is that the AI has no exact memory of the copyrighted material.
But if that's the case, then these pixels shouldn't need be patched. Because it wouldn't remember the material that spawned them.
Is just the argument I assume would be used.
It’s like training an artist who’s never seen a banana or a fire hydrant, by passing them pictures of fire hydrants labelled “this is a banana”. When you ask for a banana, you’ll get a fire hydrant. Correcting that mistake doesn’t mean “undoing pixels”, it means teaching the AI what bananas and fire hydrants are.
Well, I guess we'll see how that argument plays in court. I don't see how it follows, myself.
What is “patching pixels” and who would do it?
Explain
In order to violate copyright you need to copy the copyrighted material. Training an AI model doesn't do that.
No! It’s not using an internal exploit, it’s rather about finding a way to visually represent almost the same image, but instead using latent features with different artists (e.g, which would confuse a dreambooth+lora training), however, the method they proposed is flawed, I commented more on lemmy.world/comment/4770884
MxM111@kbin.social 1 year ago
Obviously, with so many different AIs, this can not be a factor (a bug).
If you have no problem looking at the image, then AI would not either. After all both you and AI are neural networks.
skulblaka@kbin.social 1 year ago
The neural network of a human and of an AI operate in fundamentally different ways. They also interact with an image in fundamentally different ways.
MxM111@kbin.social 1 year ago
I would not call it “fundamentally” different at all. Compared to, say, regular computer running non-neural network based program, they are quite similar, and have similar properties. They can make a mistake, hallucinate, etc.
kayrae_42@lemmy.world 1 year ago
As a person who has done machine learning, and some ai training and who has a psychotic disorder I hate they call it hallucinations. It’s not hallucinations. Human hallucinations and ai hallucinations are different things. One is based of limited data , bias, or a bad data set with builds a fundamentally bad neural network connection which can be repaired. The other is something that can not be repaired, you are not working with bad data, your brain can’t filter out data correctly and you are building wrong connections. It’s like an overdrive of input and connections that are all wrong. So you’re seeing things, hearing things, or believing things that aren’t real. You make logical leaps that are irrational and not true and reality splits for you. While similarities exist, one is because people input data wrong, or because they cleaned it wrong, or didn’t have enough. And the other is because the human brain has wiring problem caused by a variety of factors. It’s insulting and it also humanizes computers to much and degrades people with this illness.
driving_crooner@lemmy.eco.br 1 year ago
An AI don’t see the images like we do, an AI see a matrix of RGB values and the relationship they have with each other and create an statistical model of the color value of each pixel for a determined prompt.
lloram239@feddit.de 1 year ago
That’s not quite how it works. The pixels are just the first layer. Those get broken down into edges. The edges get broken down into shape. The shapes get broken down into features like eyes, noses, etc. Those get broken down into faces. And so on. It’s hierarchical feature detection. Which also happens to be what the human brain does.
The actual “drawing” the AI does is quite a bit different however. The diffusion works by starting with random noise and then gradually denoising it until an image emerges. While humans can approach painting that way, it’s rather rarely done so.