Wednesday, October 30, 2024
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The AI backlash begins: artists could protect against plagiarism with this powerful tool


A team of researchers at the University of Chicago has created a tool aimed to help online artists “fight back against AI companies” by inserting, in essence, poison pills into their original work.

Called Nightshade, after the family of toxic plants, the software is said to introduce poisonous pixels to digital art that messes with the way generative AIs interpret them. The way models like Stable Diffusion work is they scour the internet, picking up as many images as they can to use as training data. What Nightshade does is exploit this “security vulnerability”. As explained by the MIT Technology Review, these “poisoned data samples can manipulate models into learning” the wrong thing. For example, it could see a picture of a dog as a cat or a car as a cow.

Poison tactics

As part of the testing phase, the team fed Stable Diffusion infected content and “then prompted it to create images of dogs”. After being given 50 samples, the AI generated pictures of misshapen dogs with six legs. After 100, you begin to see something resembling a cat. Once it was given 300, dogs became full-fledged cats. Below, you’ll see the other trials.

Nightshade tests

(Image credit: University of Chicago/MIT Technology Review)

The report goes on to say Nightshade also affects “tangentially related” ideas because generative AIs are good “at making connections between words”. Messing with the word “dog” jumbles similar concepts like puppy, husky, or wolf. This extends to art styles as well. 

Nightshade's tangentially related samples

(Image credit: University of Chicago/MIT Technology Review)

It is possible for AI companies to remove the toxic pixels. However as the MIT post points out, it is “very difficult to remove them”. Developers would have to “find and delete each corrupted sample.” To give you an idea of how tough this would be, a 1080p image has over two million pixels. If that wasn’t difficult enough, these models “are trained on billions of data samples.” So imagine looking through a sea of pixels to find the handful messing with the AI engine.



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