Technology

This system runs on the assumption that A) massive generalized scraping is still required B) You maintain the metadata of the original image C) No transformation has occurred to the poisoned picture prior to training(Stable diffusion is 512x512). Nowhere in the linked paper did they say they had conditioned the poisoned data to conform to the data set. This appears to be a case of fighting the last war.

Takes image, applies antialiasing and resize

Oh, look at that, defeated by the completely normal process of preparing the image for training

Unfortunately for them there’s a lot of jobs dedicated to cleaning data so I’m not sure if this would even be effective. Plus there’s an overwhelming amount of data that isn’t “poisoned” so it would just get drowned out if never caught

nightshade and glaze never worked. its scam lol

Imagine if writers did the same things by writing gibberish.

At some point, it becomes pretty easy to devalue that content and create other systems to filter it.

Artists and writers should be entitled to compensation for using their works to train these models, just like any other commercial use would. But, you know, strict, brutal free-market capitalism for us, not the mega corps who are using it because "AI".

Let's see how long before someone figures out how to poison, so it returns NSFW Images

This doesn't actually work. It doesn't even need ingestion to do anything special to avoid.

Let's say you draw cartoon pictures of cats.

And your friend draws pointillist images of cats.

If you and your friend don't coordinate, it's possible you'll bias your cat images to look like dogs in the data but your friend will bias their images to look like horses.

Now each of your biasing efforts become noise and not signal.

Then you need to consider if you are also biasing 'cartoon' and 'pointillism' attributes as well, and need to coordinate with the majority of other people making cartoon or pointillist images.

When you consider the number of different attributes that need to be biased for a given image and the compounding number of coordinations that would need to be made at scale to be effective, this is just a nonsense initiative that was an interesting research paper in lab conditions but is the equivalent of a mouse model or in vitro cancer cure being taken up by naturopaths as if it's going to work in humans.

So it sounds like they are taking the image data and altering it to get this to work and the image still looks the same just the data is different. So, couldn't the ai companies take screenshots of the image to get around this?

The general term for this is adversarial input, and we've seen published reports about it since 2011 when ot was considered a threat if CSAM could be overlayed with secondary images so they weren't recognized by Google image filters or CSAM image trackers. If Apple went through with their plan to scan private iCloud accounts for CSAM we may have seen this development.

So far (AFAIK) we've not seen adversarial overlays on CSAM though in China the technique is used to deter trackng by facial recognition. Images on social media are overlaid by human rights activists / mischief-makers so that social media pics fail to match secirity footage.

The thing is like an invisible watermark, these processes are easy to detect (and reverse) once users are aware they're a thing. So if a generative AI project is aware that some images may be poisoned, it's just a matter of adding a detection and removal process to the pathway from candidate image to training database.

Similarly, once enough people start poisoning their social media images, the data scrapers will start scaning and removing overlays even before the database sets are sold to law enforcement and commercial interests.