Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis (2209.08891v3)

Abstract: 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.

An Analysis of Cultural Biases in Text-to-Image Synthesis Via Homoglyph Manipulations

The paper "Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis" provides a detailed examination of the intriguing phenomenon where models for text-to-image synthesis demonstrate susceptibility to character encodings, specifically focusing on the role of non-Latin homoglyphs. The core concept explored in the paper is the identification of cultural biases ingrained in models such as DALL-E~2 and Stable Diffusion when they are presented with textual prompts containing non-Latin characters. By manipulating individual characters within text prompts, the authors reveal how these models reflect and amplify cultural biases through generated images.

Key Findings and Contributions

1. Cultural Bias Induction:
   • The paper demonstrates that inserting a single non-Latin homoglyph into a text prompt can lead to significant cultural biases in the outputs of text-to-image synthesis models. These biases manifest in various domains, including ethnicity, architecture, and cultural symbolism, potentially altering the fair representation of images.
2. Empirical Results:
   • The authors employed both qualitative and quantitative analyses to verify the presence of cultural biases. Metrics such as Relative Bias and VQA Score were used to quantify biases across different non-Latin characters, revealing notable variations influenced by specific scripts.
3. Origin of Bias:
   • Through detailed investigations, the paper identifies the text encoder as the primary source of these biases. The ability of text encoders to distinguish between different scripts suggests an implicit association with specific cultural contexts during training.
4. Methodology for Robustness:
   • To alleviate the adverse effects of such biases, the authors propose a "homoglyph unlearning" approach. By fine-tuning the text encoder to interpret homoglyphs akin to their Latin counterparts, they achieved reduced bias while maintaining the model's overall utility.
5. Social Impact and Ethical Considerations:
   • The dual nature of homoglyph-induced biases is discussed, highlighting them as both a potential feature for expressing cultural diversity and as a vulnerability that could be maliciously exploited to reinforce undesirable stereotypes. The paper emphasizes responsible use and offers solutions like API-based character filters and training on multilingual datasets.

Implications and Future Directions

The findings of this paper have significant implications for the development and deployment of text-to-image synthesis models. Recognizing the intricacies of cultural biases at the character level can influence how models are trained, prompting a shift towards more inclusive and diverse datasets. Furthermore, techniques such as homoglyph unlearning underscore the potential for improving model robustness post-training, a valuable approach in the ongoing effort to achieve fair machine learning systems.

The paper opens avenues for future research, including assessing homoglyph impacts on other generative models, integrating multilingual training from inception, and exploring the role of text encoders in cultural representation further. As the adoption of generative models increases across varied applications, understanding and addressing the subtle biases they harbor becomes pivotal to safeguarding against unintentional harm and promoting equitable technology.

Overall, the authors provide a rigorous analysis that advances our understanding of cultural biases within multimodal AI systems, underscoring the delicate balance between model capability and ethical responsibility.