  Modern diffusion models have set the state-of-the-art in AI image generation.
Their success is due, in part, to training on Internet-scale data which often
includes copyrighted work. This prompts questions about the extent to which
these models learn from, imitate, or copy the work of human artists. This work
suggests that tying copyright liability to the capabilities of the model may be
useful given the evolving ecosystem of generative models. Specifically, much of
the legal analysis of copyright and generative systems focuses on the use of
protected data for training. As a result, the connections between data,
training, and the system are often obscured. In our approach, we consider
simple image classification techniques to measure a model's ability to imitate
specific artists. Specifically, we use Contrastive Language-Image Pretrained
(CLIP) encoders to classify images in a zero-shot fashion. Our process first
prompts a model to imitate a specific artist. Then, we test whether CLIP can be
used to reclassify the artist (or the artist's work) from the imitation. If
these tests match the imitation back to the original artist, this suggests the
model can imitate that artist's expression. Our approach is simple and
quantitative. Furthermore, it uses standard techniques and does not require
additional training. We demonstrate our approach with an audit of Stable
Diffusion's capacity to imitate 70 professional digital artists with
copyrighted work online. When Stable Diffusion is prompted to imitate an artist
from this set, we find that the artist can be identified from the imitation
with an average accuracy of 81.0%. Finally, we also show that a sample of the
artist's work can be matched to these imitation images with a high degree of
statistical reliability. Overall, these results suggest that Stable Diffusion
is broadly successful at imitating individual human artists.
