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+# Glaze - Style Protection Against AI Mimicry
+
+**Glaze** is a tool developed by the University of Chicago that adds imperceptible perturbations to digital artwork. These perturbations protect an artist’s unique visual style from being mimicked by text-to-image generative models while remaining visually identical to human viewers. It is one of the most mature and widely adopted image-level defenses available to individual creators.
+
+## Why Glaze Matters
+
+AI image models are trained to extract and reproduce artistic styles. Once a style is learned, models can generate new works “in the style of” a specific artist without permission. Glaze disrupts this process at the feature level by shifting the image in CLIP-space so that the model associates the artist’s work with a different style. The change is invisible to humans but causes the model to fail at accurate style mimicry.
+
+This technique directly counters the “style theft” problem highlighted in the primary dissertation and gives individual artists a practical, self-deployable tool that does not require cooperation from AI labs.
+
+## How It Fits the Defense Stack
+
+1. **Anubis + Nepenthes** - Web-level protection (prevent initial scraping).
+2. **Canary tokens & active denial** - Attribution and real-time cost imposition.
+3. **Glaze** (this document) - Post-scraping style protection for images that do get ingested.
+4. **Nightshade** (`nightshade.md`) - Stronger concept-level poisoning (complementary to Glaze).
+
+Glaze is the image-specific counterpart to the text and metadata techniques already documented. It is especially valuable for photographers, illustrators, and painters who publish work online.
+
+## Key Benefits for Individuals
+
+- **Invisible to humans** - No visible degradation of the original artwork.
+- **Survives common preprocessing** - Robust against resizing, compression, and format conversion.
+- **Easy to use** - Desktop application with simple “protect” workflow.
+- **Free for non-commercial use** - Accessible to independent artists.
+- **Open research** - Backed by peer-reviewed work (USENIX Security 2023).
+
+## Official Resources
+
+- Project: https://glaze.cs.uchicago.edu/
+- Paper: “Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models,” USENIX Security 2023
+
+## Recommended Starting Point
+
+Run Glaze on every publicly shared image before upload. Combine with daily canary tokens in metadata and the aggressive-bot conditional serving logic so that known scrapers receive Glazed + canaried versions. This creates a multi-layered defense that protects both the web presence and any images that are successfully stolen.
+
+*Glaze is the primary style-protection tool in the image defense layer. It is designed to be used alongside Nightshade for stronger protection.*
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