Nightshade is a University of Chicago research tool that alters images so that, if a model trainer includes them in a text-to-image training set, the model may learn incorrect associations for targeted concepts. It does not stop someone from scraping an image, force anyone to use it in training, or guarantee an effect on a particular AI service. It is distinct from Glaze, which is designed to make an artist’s style harder for a model to imitate.
What is Nightshade?
Nightshade creates what its developers call a “poison” image: a modified image intended to mislead a training model about the relationship between an image and a concept. If such an image is included in training, the intended result is that the model learns an unintended association and may produce different results for prompts related to that concept. The project’s explanation of Nightshade describes this as a way to give artists a technical means of pushing back against unauthorized use of their work in model training.
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The effect is conditional. Nightshade cannot prevent an image from being copied or scraped, and it cannot ensure that a trainer puts the image into a dataset. The impact also depends on the model, training process, and other data involved.
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How does Nightshade work?
At a high level, Nightshade makes image changes intended to be difficult for people to notice but meaningful to a machine-learning model. The image is tagged with a target concept; if a model trains on the altered image, the poison is intended to distort what the model learns about that concept. This is a training-time intervention, not a filter that blocks a prompt or a shield that prevents every form of copying.
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A 2023 Nightshade paper reported controlled experiments on text-to-image models. In the tested settings, the authors found that fewer than 100 poison samples could corrupt an SDXL prompt; the paper also reported high attack success in experiments using 200 samples. These are results from the authors’ specific experimental setups, not a universal minimum or a prediction that the same number of images will affect any current commercial model. Read the 2023 Nightshade paper.
Nightshade vs. Glaze: what is the difference?
The tools are related and can be used together according to their developers, but they address different goals. Glaze is meant to interfere with a model’s ability to learn and reproduce an individual artist’s style, particularly through fine-tuning. Nightshade is meant to poison a model’s learned associations for targeted concepts when it trains on altered images.
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| Question | Glaze | Nightshade |
|---|---|---|
| Primary target | Individualized artist-style imitation. | Learned associations for targeted concepts in a model trained on poison samples. |
| When it is intended to act | When a model learns an artist’s style, especially through fine-tuning on that artist’s work. | When a model’s training includes the altered poison image. |
| Access described by the project | Runs locally; WebGlaze is an alternative for artists without suitable local hardware. | The Nightshade guide provides the image-poisoning workflow. |
| Key limitation | Not reliable protection against every image-to-image edit, style transfer, or inpainting workflow; future techniques may overcome current defenses. | Cannot make a trainer use an image or guarantee an effect on a particular model or service. |
The University of Chicago’s 2023 launch coverage said Glaze was tested with four currently working artists and 195 historical artists. More than 1,000 professional and part-time artists were surveyed during development, and over 90 percent said they were willing to use the software after viewing its results. Those figures describe that launch coverage and survey, not all artists. University of Chicago News reported the launch and survey on February 14, 2023.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsWhat should artists check before running Nightshade?
The Nightshade guide recommends checking that the poison tag identifies the single key concept or object in the image. It also describes a trade-off: greater intensity is generally associated with stronger intended poison effects, but can make changes more visible. Review the processed image before posting it, especially if subtle changes to color, edges, texture, or composition would matter to the work. See the Nightshade user guide.
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What are Glaze’s practical limits?
Glaze’s own documentation says its changes can be more visible in flat-color artwork and on smooth backgrounds. It is less effective against imitation of styles already represented in a model’s base training. The project also warns that future methods may overcome current defenses. The Glaze project page explains its design and limitations.
Glaze is not a reliable general-purpose defense against image-to-image editing, style transfer, or inpainting. Its FAQ says that results against stronger image-to-image tools are inconsistent; it states, “At this time, we do not believe Glaze provides consistent protection against img2img attacks, including style transfer and inpainting.” The FAQ was last updated August 25, 2026. Read the Glaze FAQ.
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How can artists access Glaze and WebGlaze?
Glaze runs locally, while WebGlaze is a web-based alternative for artists without suitable hardware. The project describes WebGlaze as free and invite-only, with daily and weekly quotas. Its documentation says submitted and processed images are deleted after processing. Access and quota details can change, so check the current project information before relying on a specific workflow. Check WebGlaze’s current access and usage information.
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Should artists use Nightshade, Glaze, or both?
Choose based on the problem you are trying to address: Glaze is aimed at style imitation, while Nightshade is aimed at concept associations learned from poison samples during training. The developers recommend applying both, but that is project guidance—not proof that the combination provides comprehensive protection. Neither tool guarantees that work will not be copied, used in training, or imitated by every system.
Quick Recap
- For a goal of disrupting style imitation, review Glaze’s stated limitations and inspect how its changes affect your particular artwork.
- For a goal of poisoning training data, check Nightshade’s target tag and inspect the output image before sharing it.
- For either tool, treat published efficacy figures as results for the models and conditions actually studied, not as a guarantee for newer or commercial systems.
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