this post was submitted on 23 Oct 2023
570 points (86.4% liked)

Technology

60028 readers
3613 users here now

This is a most excellent place for technology news and articles.

Our Rules

  1. Follow the lemmy.world rules.
  2. Only tech related content.
  3. Be excellent to each another!
  4. Mod approved content bots can post up to 10 articles per day.
  5. Threads asking for personal tech support may be deleted.
  6. Politics threads may be removed.
  7. No memes allowed as posts, OK to post as comments.
  8. Only approved bots from the list below, to ask if your bot can be added please contact us.
  9. Check for duplicates before posting, duplicates may be removed

Approved Bots

founded 2 years ago
MODERATORS
L3s
[email protected]
[email protected]
L4s
enu
570
This new data poisoning tool lets artists fight back against generative AI (www.technologyreview.com)
submitted 1 year ago by ElectroVagrant to c/technology
130 comments fedilink hide all child comments
 

A new tool lets artists add invisible changes to the pixels in their art before they upload it online so that if it’s scraped into an AI training set, it can cause the resulting model to break in chaotic and unpredictable ways.

The tool, called Nightshade, is intended as a way to fight back against AI companies that use artists’ work to train their models without the creator’s permission.
[...]
Zhao’s team also developed Glaze, a tool that allows artists to “mask” their own personal style to prevent it from being scraped by AI companies. It works in a similar way to Nightshade: by changing the pixels of images in subtle ways that are invisible to the human eye but manipulate machine-learning models to interpret the image as something different from what it actually shows.

you are viewing a single comment's thread
view the rest of the comments
[–] [email protected] 0 points 1 year ago (1 children)

The problem is identifying it. If it's necessary to preprocess every image used for training instead of just feeding it is a model that already makes it much more resources costly

[–] [email protected] 1 points 1 year ago

You wouldn't want to. If you just feed it to the models, then if there are enough of these images to matter the model will learn to ignore the differences. You very specifically don't want to prevent the model from learning to overcome these things, exactly because if you do you're stuck with workarounds like that forever, but if you don't the model will just become more robust to noisy data like this.