生成合成虹膜图像可缓解数据采集难题，但存在身份泄露风险。
Synthetic Iris Image Databases and Identity Leakage: Risks and Mitigation Strategies
- 用GAN、VAE和扩散模型生成高保真虹膜图像。
- 合成数据可能泄露训练集中的个体生物特征信息。
- 提出防护策略以应对生成数据带来的隐私风险。
本文系统综述了虹膜图像生成技术，旨在缓解从真实个体收集大规模多样化生物特征数据的困难，而此类数据对生物特征识别方法的发展至关重要。生成方法涵盖传统图像处理、多种迭代的GAN生成器、变分自编码器（VAEs）以及扩散模型。本文分析了各类方法在虹膜图像生成中的潜力与保真度，并提供推断结果示例。此外，还探讨了训练集中个体生物特征信息泄露的风险，提出了若将生成方法作为真实生物特征数据集替代方案时必须实施的防范策略。
原文摘要 · Abstract (English)
This paper presents a comprehensive overview of iris image synthesis methods, which can alleviate the issues associated with gathering large, diverse datasets of biometric data from living individuals, which are considered pivotal for biometric methods development. These methods for synthesizing iris data range from traditional, hand crafted image processing-based techniques, through various iterations of GAN-based image generators, variational autoencoders (VAEs), as well as diffusion models. The potential and fidelity in iris image generation of each method is discussed and examples of inferred predictions are provided. Furthermore, the risks of individual biometric features leakage from the training sets are considered, together with possible strategies for preventing them, which have to be implemented should these generative methods be considered a valid replacement of real-world biometric datasets.
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