用扩散模型生成真实且无法识别的虹膜图像数据库。
Generating a Biometrically Unique and Realistic Iris Database
- 在开源框架中训练扩散模型，生成逼真虹膜纹理。
- 生成图像在生物特征上与训练数据无关联，且覆盖多样色素分布。
- 适合隐私敏感的虹膜研究与防欺骗攻击测试。
过去30年中，虹膜作为生物特征标识的应用大幅增加，引发研究中虹膜图像使用的隐私与安全担忧。由于伦理问题，获取虹膜图像数据库困难，阻碍了生物特征研究。本文介绍并演示如何通过在开源扩散框架中训练扩散模型，生成真实、生物特征不可识别的彩色虹膜图像数据库。我们验证了模型生成的虹膜纹理在生物特征上与训练数据无关联，且能生成完整的虹膜色素分布。结果表明，扩散网络可轻松实现上述目标，值得进一步探索其在虹膜数据库构建与活体检测攻击防御中的应用。
原文摘要 · Abstract (English)
The use of the iris as a biometric identifier has increased dramatically over the last 30 years, prompting privacy and security concerns about the use of iris images in research. It can be difficult to acquire iris image databases due to ethical concerns, and this can be a barrier for those performing biometrics research. In this paper, we describe and show how to create a database of realistic, biometrically unidentifiable colored iris images by training a diffusion model within an open-source diffusion framework. Not only were we able to verify that our model is capable of creating iris textures that are biometrically unique from the training data, but we were also able to verify that our model output creates a full distribution of realistic iris pigmentations. We highlight the fact that the utility of diffusion networks to achieve these criteria with relative ease, warrants additional research in its use within the context of iris database generation and presentation attack security.
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