论文标题
模糊的承诺为深度学习产生的生物识别模板提供了不足的保护
Fuzzy Commitments Offer Insufficient Protection to Biometric Templates Produced by Deep Learning
论文作者
论文摘要
在这项工作中，我们研究了模糊的承诺在由深度学习面部识别系统处理的面部图像应用于面部图像时提供的保护。我们表明，尽管这些系统能够产生良好的准确性，但它们会产生熵太少的模板。结果，我们提出了采用受保护模板的重建攻击，并重建面部图像。重建的面部图像非常类似于原始图像。在最简单的攻击方案中，这些重建模板中有78％以上成功解锁了帐户（当系统配置为0.1％时）。即使在“最难的”设置中（在其中我们从一个系统中获取重建的图像，并在不同的系统中使用它，具有不同的特征提取过程），重建的图像的成功率比系统的远高50至120倍。
In this work, we study the protection that fuzzy commitments offer when they are applied to facial images, processed by the state of the art deep learning facial recognition systems. We show that while these systems are capable of producing great accuracy, they produce templates of too little entropy. As a result, we present a reconstruction attack that takes a protected template, and reconstructs a facial image. The reconstructed facial images greatly resemble the original ones. In the simplest attack scenario, more than 78% of these reconstructed templates succeed in unlocking an account (when the system is configured to 0.1% FAR). Even in the "hardest" settings (in which we take a reconstructed image from one system and use it in a different system, with different feature extraction process) the reconstructed image offers 50 to 120 times higher success rates than the system's FAR.
