  Image generation models can generate or edit images from a given text. Recent
advancements in image generation technology, exemplified by DALL-E and
Midjourney, have been groundbreaking. These advanced models, despite their
impressive capabilities, are often trained on massive Internet datasets, making
them susceptible to generating content that perpetuates social stereotypes and
biases, which can lead to severe consequences. Prior research on assessing bias
within image generation models suffers from several shortcomings, including
limited accuracy, reliance on extensive human labor, and lack of comprehensive
analysis. In this paper, we propose BiasPainter, a novel evaluation framework
that can accurately, automatically and comprehensively trigger social bias in
image generation models. BiasPainter uses a diverse range of seed images of
individuals and prompts the image generation models to edit these images using
gender, race, and age-neutral queries. These queries span 62 professions, 39
activities, 57 types of objects, and 70 personality traits. The framework then
compares the edited images to the original seed images, focusing on the
significant changes related to gender, race, and age. BiasPainter adopts a key
insight that these characteristics should not be modified when subjected to
neutral prompts. Built upon this design, BiasPainter can trigger the social
bias and evaluate the fairness of image generation models. We use BiasPainter
to evaluate six widely-used image generation models, such as stable diffusion
and Midjourney. Experimental results show that BiasPainter can successfully
trigger social bias in image generation models. According to our human
evaluation, BiasPainter can achieve 90.8% accuracy on automatic bias detection,
which is significantly higher than the results reported in previous work.
