Kavli Affiliate: Yi Zhou
| First 5 Authors: Huichan Seo, Huichan Seo, , ,
| Summary:
Generative image models produce striking visuals yet often misrepresent
culture. Prior work has examined cultural bias mainly in text-to-image (T2I)
systems, leaving image-to-image (I2I) editors underexplored. We bridge this gap
with a unified evaluation across six countries, an 8-category/36-subcategory
schema, and era-aware prompts, auditing both T2I generation and I2I editing
under a standardized protocol that yields comparable diagnostics. Using open
models with fixed settings, we derive cross-country, cross-era, and
cross-category evaluations. Our framework combines standard automatic metrics,
a culture-aware retrieval-augmented VQA, and expert human judgments collected
from native reviewers. To enable reproducibility, we release the complete image
corpus, prompts, and configurations. Our study reveals three findings: (1)
under country-agnostic prompts, models default to Global-North, modern-leaning
depictions that flatten cross-country distinctions; (2) iterative I2I editing
erodes cultural fidelity even when conventional metrics remain flat or improve;
and (3) I2I models apply superficial cues (palette shifts, generic props)
rather than era-consistent, context-aware changes, often retaining source
identity for Global-South targets. These results highlight that
culture-sensitive edits remain unreliable in current systems. By releasing
standardized data, prompts, and human evaluation protocols, we provide a
reproducible, culture-centered benchmark for diagnosing and tracking cultural
bias in generative image models.
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