Technology

Just remember folks. Kutcher is a slimeball too.

The guy went from a D list star and hanging out with the likes of Danny Masterson and going to Diddy’s infamous parties - to suddenly overnight courting the US government and being the face of ‘helping’ children everywhere.

Yeah right…..

Reminds me of the A cup breasts porn ban in Australia a few years ago, because only pedos would watch that

It could also, of course, make mistakes, but Kevin Guo, Hive's CEO, told Ars that extensive testing was conducted to reduce false positives or negatives substantially. While he wouldn't share stats, he said that platforms would not be interested in a tool where "99 out of a hundred things the tool is flagging aren't correct."

I take this to mean it is at least 1% accurate lol.

Skynet's real origin story. We might just deserve judgement day.

How did you figure out it had issues with broccoli?. Were you checking your vegetable gallery for CSAM?

No. This is an inference model, not a generative model. You generally cannot train a model for both, unless you do it on purpose, and they certainly did not (especially since inference models are way easier to train than generative models).

I was going to say... Sure would be nice to have this feature in all the open source AI image generator tools but you're absolutely right 😩

IFTAS is already working with Thorn towards this goal. But you already have access to such technology through my toolset.

If AI was reliable, maybe. MAYBE. But guess what? It turns out that “advanced autocomplete” does a shitty job of most things, and I bet false positives will be numerous.

And is there any risk of people turning these kinds of models around and using them to generate images?

There isn't really much fundamental difference between an image detector and an image generator. The way image generators like stable diffusion work is essentially by generating a starting image that's nothing but random static and telling the generator "find the cat that's hidden in this noise."

It'll probably take a bit of work to rig this child porn detector up to generate images, but I could definitely imagine it happening. It's going to make an already complicated philosophical debate even more complicated.

I think image generators in general work by iteratively changing random noise and checking it with a classifier, until the resulting image has a stronger and stronger finding of “cat” or “best quality” or “realistic”.

If this classifier provides fine grained descriptive attributes, that’s a nightmare. If it just detects yes or no, that’s probably fine.

Nobody would have been looking directly at the source data. The FBI or whoever provides the dataset to approved groups, but after that you just say "use all the images in this folder" and it goes. But I don't even know if they actually provide real full-resolution images, or just perceptual hashes, or downsampled images.

And while it's possible to use the dataset to generate new images assuming the training data had full-res images, like I said, I know they investigate the people making the request before allowing access. And access is probably supervised and audited.

Available image generators are already capable of generating those images and they weren't even trained on it. Once a neural network can detect/generate two separate concepts, it can detect/generate the overlap. It won't be as fine-tuned obviously, but can still turn out scarily accurate.