Models for text-to-image synthesis, such as DALL-E~2 and Stable Diffusion,
have recently drawn a lot of interest from academia and the general public.
These models are capable of producing high-quality images that depict a variety
of concepts and styles when conditioned on textual descriptions. However, these
models adopt cultural characteristics associated with specific Unicode scripts
from their vast amount of training data, which may not be immediately apparent.
We show that by simply inserting single non-Latin characters in a textual
description, common models reflect cultural stereotypes and biases in their
generated images. We analyze this behavior both qualitatively and
quantitatively, and identify a model's text encoder as the root cause of the
phenomenon. Additionally, malicious users or service providers may try to
intentionally bias the image generation to create racist stereotypes by
replacing Latin characters with similarly-looking characters from non-Latin
scripts, so-called homoglyphs. To mitigate such unnoticed script attacks, we
propose a novel homoglyph unlearning method to fine-tune a text encoder, making
it robust against homoglyph manipulations.

通过在文本描述中插入单个非拉丁字符，我们展示了普通模型如何反映文化刻板印象和偏见。我们定性和定量分析了这种行为，并将其归因于模型的文本编码器。此外，我们提出了一种新颖的同形学习方法，通过微调文本编码器，使其能够抵抗同形符号的操纵.