Nightshade AI

What if your art could fight back against unauthorized AI training?

RecommendedNightshade is a free, research-backed tool that empowers artists to actively poison AI models, making unauthorized training on their work counterproductive for model developers.

Nightshade is a tool that transforms images into "poison" samples, making them unsuitable for AI model training. It introduces subtle, human-imperceptible changes that cause generative AI models to learn unpredictable and incorrect behaviors when trained on them. This aims to deter unauthorized scraping and training of artists' work.

Key Features:
  • Transforms images into 'poison' samples for AI models
  • Subtle visual changes, largely imperceptible to human eyes
  • Robust against common image transformations (cropping, resampling, compression)
  • Designed to disrupt feature representations within generative AI models
  • Can be used in conjunction with Glaze for comprehensive protection
Pros
  • Artists concerned about unauthorized AI model training on their work
  • Artists who want to disincentivize AI companies from scraping their images
  • Artists who want to contribute to a collective defense against AI exploitation
Cons
  • More visible changes on art with flat colors and smooth backgrounds at higher intensities
  • Not future-proof indefinitely, but designed to evolve with countermeasures
  • Requires GPU for faster processing, otherwise CPU mode can be slow
Pricing
freeFree tier
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Quick Decision
Try if: You are an artist concerned about generative AI models being trained on your work without consent and want to actively disrupt this process. You understand the tool's purpose is offensive poisoning rather than defensive style protection.
Skip if: You are looking for a tool to protect your art from style mimicry (Glaze is better for this) or you are an AI developer looking for training data.
Not for: Users looking for a tool to generate AI art; Artists primarily concerned with preventing style mimicry (use Glaze for that)
Trust Signals
  • Founded


    2023
  • Users


    2.5M+ downloads (since Jan 2024)
  • Team Size


    small
  • Funding


    Research grants and donations (NSF, DARPA, Amazon AWS, C3.ai)
Tech Details
Platforms
desktop
Open Source
No
Support
Channels
email
Company
  • Name


    SAND Lab, University of Chicago
  • Location


    Chicago, IL, USA

A research effort developing technical tools to protect human creatives against invasive uses of generative AI.

FAQ

What is the difference between Nightshade and Glaze?

Glaze is a defensive tool for individual artists to protect against style mimicry, while Nightshade is an offensive tool for artists to collectively disrupt models that scrape images without consent. Nightshade aims to poison models, making them learn incorrect behaviors, whereas Glaze aims to make it difficult for models to mimic an artist's style.

Is Nightshade illegal or can users be sued for using it?

According to the Nightshade team, they have consulted with lawyers who have no concerns about the legality of creating or using Nightshade to protect one's own art. They liken it to putting hot sauce in your clearly labeled lunch; if someone steals it and gets sick, you are not liable. Nightshade is not a virus and only affects diffusion models when a lot of shaded images are trained.

How does Nightshade affect the visual quality of an image?

Nightshade uses similar or even lower intensity perturbation values as Glaze, meaning visual artifacts are at most equivalent to Glaze, and often more subtle. There is a low intensity setting available for those prioritizing the visual quality of the original image.

Does Nightshade scrape, copy, or train on my art?

Absolutely not. Nightshade is designed to work offline and does not collect any user data or art. The team explicitly states they are not interested in profit, selling IP, or model training, and have been working to fight for the rights of human artists.

Can Nightshade be bypassed by pixel-smoothers or other transformations?

No, Nightshade's effects are robust. It alters the large majority of pixels (80%+) in an image, not just a few. Smoothing out visible artifacts only changes a very small portion of the overall alterations, similar to rearranging chairs in a house that has been moved by an earthquake.

Use Cases
  • Artists concerned about unauthorized AI model training on their work
  • Artists who want to disincentivize AI companies from scraping their images
  • Artists who want to contribute to a collective defense against AI exploitation
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