Analyzing Bias in Diffusion-based Face Generation Models
Malsha V. Perera Vishal M. Patel
Diffusion models are becoming increasingly popular in synthetic data
generation and image editing applications. However, these models can amplify
existing biases and propagate them to downstream applications. Therefore, it is
crucial to understand the sources of bias in their outputs. In this paper, we
investigate the presence of bias in diffusion-based face generation models with
respect to attributes such as gender, race, and age. Moreover, we examine how
dataset size affects the attribute composition and perceptual quality of both
diffusion and Generative Adversarial Network (GAN) based face generation models
across various attribute classes. Our findings suggest that diffusion models
tend to worsen distribution bias in the training data for various attributes,
which is heavily influenced by the size of the dataset. Conversely, GAN models
trained on balanced datasets with a larger number of samples show less bias
across different attributes.