Asked to produce “a landscape by Thomas Kinkade,” a text-to-image generator fine-tuned on the pastoral painter’s work can mimic his style in seconds, often for pennies. A new technique aims to make it harder for algorithms to mimic an artist’s style.

What’s new: Shawn Shan and colleagues at University of Chicago unveiled Glaze, a tool that imperceptibly alters works of art to prevent machine learning models from learning the artist's style from them. You can download it here.

Key insight: Art style depends on many factors (color, shape, form, space, texture, and others). Some styles tend not to blend easily. For instance, a portrait can’t show both the sharp edges of a photograph and the oil-paint strokes of Vincent Van Gogh. Trained models have encountered few, if any, such blends, so they tend not to be able to mimic them accurately. But the ability of text-to-image generators to translate images into a different style (by prompting them with words like “. . . in the style of Van Gough”) makes it possible to alter a photorealistic portrait imperceptibly to make some pixels more like an oil painting (or vice-versa). Fine-tuned on such alterations, a text-to-image generator that’s prompted to imitate them will produce an incoherent blend that differs notably from the original style.

How it works: Glaze makes an artist’s images more similar to images of a very different style. The difference derails image generators while being imperceptible to the human eye.

  • Glaze uses embeddings previously generated by Stable Diffusion. That model’s image encoder generated embeddings of works by more than 1,000 celebrated artists. Then it generated an embedding of each artist by computing the centroid of the embeddings of the artist’s works.
  • Given works by a new artist, Glaze uses Stable Diffusion to generate an artist embedding in the same way.
  • Glaze compares the new artist’s embedding with those of other artists using an undescribed method. It chooses an artist whose embedding is between the most distant 50 percent to 75 percent.
  • Glaze uses Stable Diffusion to translate each of the new artist’s works into the chosen artist’s style.
  • For each of the new artist’s works, Glaze learns a small perturbation (a learned vector) and uses it to modify the pixels in the original work. In doing so, it minimizes the difference between the embeddings of the perturbed work and style-transferred version. To avoid changing the work too much, it keeps the vector’s magnitude (that is, the perturbation’s cumulative effect) below a certain threshold.

Results: The authors fine-tuned Stable Diffusion on Glaze-modified works by 13 artists of various styles and historical periods. Roughly 1,100 artists evaluated groups of four original and four mimicked works and rated how well Glaze protected an artist’s style (that is, how poorly Stable Diffusion mimicked the artist). 93.3 percent of evaluators found that Glaze successfully protected the style, while 4.6 percent judged that a separate Stable Diffusion fine-tuned on unmodified art was protective.

Yes, but: It’s an open question whether Glaze works regardless of the combination of models used to produce embeddings, perform style transfer, and generate images. The authors’ tests were limited in this regard.

Why it matters: As AI extends its reach into the arts, copyright law doesn’t yet address the use of creative works to train AI systems. Glaze enables artists to have a greater say in how their works can be used — by Stable Diffusion, at least.

We’re thinking: While technology can give artists some measure of protection against stylistic appropriation by AI models, ultimately society at large must resolve questions about what is and isn't fair. Thoughtful regulation would be better than a cat-and-mouse game between artists and developers.


Subscribe to The Batch

Stay updated with weekly AI News and Insights delivered to your inbox