Biometric authentication systems are crucial for security, but developing them involves various complexities, including privacy, security, and achieving high accuracy without directly storing pure biometric data in storage. We introduce an innovative image distortion technique that makes facial images unrecognizable to the eye but still identifiable by any custom embedding neural network model. Using the proposed approach, we test the reliability of biometric recognition networks by determining the maximum image distortion that does not change the predicted identity. Through experiments on MNIST and LFW datasets, we assess its effectiveness and compare it based on the traditional comparison metrics.

翻译：生物特征认证系统对安全至关重要，但其开发涉及隐私保护、安全性以及在不直接存储原始生物特征数据的前提下实现高精度识别等多重复杂性。我们提出一种创新图像失真技术，该技术能使面部图像在肉眼不可识别的同时，仍可被任意定制嵌入神经网络模型辨识。通过所提方法，我们通过确定不改变预测身份的最大图像失真阈值，检验生物特征识别网络的可靠性。基于MNIST和LFW数据集的实验，我们评估了该方法的有效性，并与传统对比指标进行了比较分析。