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 metamorphic testing
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 any changes related to gender, race, and age. BiasPainter adopts a testing
oracle 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. To evaluate the
effectiveness of BiasPainter, we use BiasPainter to test five widely-used
commercial image generation software and models, such as stable diffusion and
Midjourney. Experimental results show that 100% of the generated test cases
can successfully trigger social bias in image generation models.
Expert Commentary: Unveiling Bias in Image Generation Models
Image generation technology has made significant advancements in recent years, with models like DALL-E and Midjourney pushing the boundaries of what is possible in generating and editing images from text descriptions. However, these cutting-edge models bring with them potential risks in the form of perpetuating social biases and stereotypes. This is due to their reliance on expansive Internet datasets, which can inadvertently encode biased content.
Past attempts to assess bias in image generation models have faced limitations in terms of accuracy, labor requirements, and comprehensive analysis. To address these shortcomings, the researchers propose a novel metamorphic testing framework called BiasPainter. This framework aims to automatically and accurately trigger social bias in image generation models.
BiasPainter achieves this by utilizing a diverse set of seed images representing individuals from various backgrounds. The models are prompted to edit these images using gender, race, and age-neutral queries, encompassing a wide range of professions, activities, objects, and personality traits. The edited images are then compared to the original seed images, focusing on any changes related to gender, race, and age.
By adopting a testing oracle that remains neutral to these characteristics, BiasPainter can effectively detect and evaluate the presence of social bias within image generation models. This metamorphic testing approach provides a more robust and automated means of identifying biases compared to previous manual assessments.
The researchers evaluated BiasPainter by applying it to five widely-used commercial image generation software and models, including stable diffusion and Midjourney. The experimental results demonstrate that BiasPainter successfully triggered social bias in 100% of the generated test cases.
Multi-Disciplinary Nature and Wider Field Relevance
The research presented in this paper showcases the multi-disciplinary nature of exploring bias in multimedia information systems. It combines expertise from fields such as artificial intelligence, computer vision, and ethics. By uncovering and addressing biases in image generation models, this research contributes to the wider field of multimedia information systems by highlighting the importance of ethical considerations in the development and deployment of such technologies.
Furthermore, the concepts explored in this paper have direct relevance to animation, artificial reality, augmented reality, and virtual realities. Image generation models are fundamental building blocks in these fields, and their biases can potentially influence the visual content created and experienced by users. Understanding and mitigating these biases is crucial for developing immersive and inclusive multimedia experiences.
In conclusion, BiasPainter presents a significant step forward in addressing bias issues within image generation models. Its metamorphic testing framework offers a comprehensive and automated approach to trigger social biases and assess the fairness of such models. The findings from this research emphasize the need for continued exploration and mitigation of biases in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities to ensure equitable and unbiased user experiences.