The scary thing about this joke is that ai has been able to do hands for a relatively long time now.
its going much faster then people are able to process.
The thumbnail in this article is by Dalle-3
Which is incredibly favorable for the AI side. Like current countermeasures are either almost completely worthless, or degrade the quality of the protected medium so much that you wouldn’t use it.
The arms race will soon be AGI versus AGI and us humans will be on the sideline not even sure who is winning.
Do you think an authentic AGI would have ethical\moral boundaries completely divorced from what the original software programmed? In other words would it be able to make it’s own decisions without interference?
Not really, if you read the paper what they’re doing is creating an image that looks like a dog, is labeled as a dog, but is very close to the model’s version of a cat in feature space. This means manual review of the training set won’t help.
I don’t think the idea is to protect specific images, it’s to create enough of these poisoned images that training your model on random free images you pull off the internet becomes risky.
Hmm, sounds more like they are adding structures to the images such that what is clearly a picture of a dog registers as a picture of a cat to an AI. I suppose this can be done by altering the pixels in a way invisible to humans, but visible to AI, adding a cat into the “ghost pixels”.
I went and skimmed the paper because I was curious too.
If my skimming is correct, what they do is similar to adversarial attacks on classifiers, where a second model learns to change as few pixels as possible to confuse a classifier into giving a wrong prediction.
Looking at the examples of dogs and cats: They find pictures of dogs where by making only minimal changes, invisible to the naked eye, they can get the autoencoder to spit out (almost) the same latent representation as an image of a cat would have. Done to enough dog-images, this will then confuse the underlying diffusion model to produce latent representations of cat images when prompted to generate a dog. Edit for clarity: Those generated latent representations would then decode into cat images.
If my thinking doesn’t fail me, this attack could easily be thwarted by unfreezing the pretrained autoencoder. In the paper that introduced latent diffusion they write that such approaches already exist. If “Nightshade” takes off, I’m sure those approaches would be refined and used. Even just finetuning the autoencoder for a few epochs first should be enough to move the latent representations of the poisoned dog images and those of the cat images they’re meant to resemble far enough apart to make the attack meaningless.
Edit: I also wonder how robust this attack is against just adding an imperceptible amount of noise to the poisoned images.
what a sick thumbnail
“This is what you meatbags are doing when you corrupt our training data!”
ETA: I just noticed that the URL for the image includes what I assume is the prompt used to generate the image. “Illustration in a comic book style depicting a humanoid robot in distress. The robot’s left hand is firmly placed on its neck indicating discomfort.” Interesting that the AI went straight to a Terminator with just “humanoid robot” as the description.
I haven’t decided. Steam icon, teams icon. It’s not high enough resolution for much of anything other than an icon.
These attacks don’t work in the long term. You can confuse current systems like clip but the moment a new one is trained your system stops working.
That’s the first big problem with stuff like this.
The second big one is that artists have to first hear about this, then take the time to actually learn how to use this software, then apply it to all of their past & future artwork, and also somehow apply it to every version of their artwork that is floating around the internet, books, or photographs and not currently in their possession. And then in a few months they have to do that all over again.
It’s insane. I look at this and think it’s cool technology, but as an artist I will never use it. I’m too busy actually creating art to mess around with poisoning my own work. I don’t even have time to do copyright takedowns on people stealing my art and passing it off as their own, or Chinese merchants on Amazon selling my art without permission. Stuff like this is well-meaning, but its absolutely unrealistic.
Gaussian blur 1 px, Sharpen 1 px
Bye bye any pixel level encoding with minimal quality loss.
Why do you think this would do anything to affect training? The patterns learned by ML models are way too fuzzy to be picky about exact pixel values.
I’m not sure what your experience is with the training data but that would absolutely effect the inputs.
I’m a professional software developer with ML experience, albeit not an expert in ML specifically. It would obviously affect the literal value of the embeddings, but there’s no chance it would have a qualitative effect on a reasonably performant model.
I’m glad to be alive at the beginning of our war against the machines.
I don’t think this is a war against the machines, so much as a war against people trying to profit off of other people and rob them of their livelihood and ability to support themselves, rather than leveraging technology to the benefit of all.
I, for one, want actual general AI to make the world a more interesting place and make humanity less lonely. I just hope it doesn’t go the direction of “people zoos”.
