#RESIST: Data poisoning: how artists are sabotaging AI to take revenge on image generators.

Researchers who want to empower individual artists have recently created a tool named “Nightshade” to fight back against unauthorised image scraping.

The tool works by subtly altering an image’s pixels in a way that wreaks havoc to computer vision but leaves the image unaltered to a human’s eyes.

If an organisation then scrapes one of these images to train a future AI model, its data pool becomes “poisoned”. This can result in the algorithm mistakenly learning to classify an image as something a human would visually know to be untrue. As a result, the generator can start returning unpredictable and unintended results.

As in our earlier example, a balloon might become an egg. A request for an image in the style of Monet might instead return an image in the style of Picasso.

Some of the issues with earlier AI models, such as trouble accurately rendering hands, for example, could return. The models could also introduce other odd and illogical features to images – think six-legged dogs or deformed couches.

The higher the number of “poisoned” images in the training data, the greater the disruption. Because of how generative AI works, the damage from “poisoned” images also affects related prompt keywords.

I’m curious how pixels are “altered” but the article is skimpy on the details.