Banana100: 通过100次迭代图像复制打破NR-IQA度量标准
Banana100: Breaking NR-IQA Metrics by 100 Iterative Image Replications with Nano Banana Pro
AI总结：
Banana100通过100次迭代编辑生成28000张退化图像，揭示多轮编辑中图像质量退化问题，发现现有NR-IQA度量标准无法检测严重退化图像，威胁未来模型训练稳定性。
摘要：
多模态代理系统多步骤迭代图像编辑能力已改变数字内容创作。尽管最新图像编辑模型在单轮编辑中能忠实执行指令并生成高质量图像，但多轮编辑中图像质量退化问题显著。随着图像反复编辑，微小伪影累积，迅速导致可见噪声严重积累且无法遵循简单编辑指令。为系统研究这些失败，我们引入Banana100，包含28000张通过100次迭代编辑步骤生成的退化图像，涵盖多样纹理和图像内容。令人震惊的是，图像质量评估者无法检测退化。在21种流行的无参考图像质量评估（NR-IQA）度量标准中，没有一种能始终将严重退化图像评分低于干净图像。生成器和评估器的双重失败可能威胁未来模型训练的稳定性以及部署代理系统的安全性，如果由多轮编辑生成的低质量合成数据逃过质量过滤。我们发布完整代码和数据，以促进更稳健模型的发展，帮助缓解多模态代理系统脆弱性。
英文摘要：
The multi-step, iterative image editing capabilities of multi-modal agentic systems have transformed digital content creation. Although latest image editing models faithfully follow instructions and generate high-quality images in single-turn edits, we identify a critical weakness in multi-turn editing, which is the iterative degradation of image quality. As images are repeatedly edited, minor artifacts accumulate, rapidly leading to a severe accumulation of visible noise and a failure to follow simple editing instructions. To systematically study these failures, we introduce Banana100, a comprehensive dataset of 28,000 degraded images generated through 100 iterative editing steps, including diverse textures and image content. Alarmingly, image quality evaluators fail to detect the degradation. Among 21 popular no-reference image quality assessment (NR-IQA) metrics, none of them consistently assign lower scores to heavily degraded images than to clean ones. The dual failures of generators and evaluators may threaten the stability of future model training and the safety of deployed agentic systems, if the low-quality synthetic data generated by multi-turn edits escape quality filters. We release the full code and data to facilitate the development of more robust models, helping to mitigate the fragility of multi-modal agentic systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
