Abstract
Accurate age verification can protect underage users from unauthorized access to online platforms and e-commerce sites that provide age-restricted services. However, accurate age estimation can be confounded by several factors, including facial makeup that can induce changes to alter perceived identity and age to fool both humans and machines. In this work, we propose DiffClean which erases makeup traces using a text-guided diffusion model to defend against makeup attacks. DiffClean improves age estimation (minor vs. adult accuracy by 4.8%) and face verification (TMR by 8.9% at FMR=0.01%) over competing baselines on digitally simulated and real makeup images.
Abstract (translated)
准确的年龄验证可以保护未成年人免受未经授权访问提供年龄限制服务的在线平台和电子商务网站。然而，准确的年龄估计可能会受到多种因素的影响，包括面部化妆，这些因素可以改变感知的身份和年龄，从而欺骗人类和机器。在这项工作中，我们提出了DiffClean，这是一种使用文本引导扩散模型擦除妆容痕迹的方法，以防御化妆攻击。在数字模拟和真实化妆品图像上，与竞争基线相比，DiffClean提高了年龄估计的准确性（未成年人与成人的准确率提高了4.8%）以及面部验证的性能（FMR=0.01%时TMR提高了8.9%）。
URL
https://arxiv.org/abs/2507.13292
