In the realm of security applications, biometric authentication systems play a crucial role, yet one often encounters challenges concerning privacy and security while developing one. One of the most fundamental challenges lies in avoiding storing biometrics directly in the storage but still achieving decently high accuracy. Addressing this issue, we contribute to both artificial intelligence and engineering fields. We introduce an innovative image distortion technique that effectively renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models. From the theoretical perspective, we explore how reliable state-of-the-art biometrics recognition neural networks are by checking the maximal degree of image distortion, which leaves the predicted identity unchanged. On the other hand, applying this technique demonstrates a practical solution to the engineering challenge of balancing security, precision, and performance in biometric authentication systems. Through experimenting on the widely used datasets, we assess the effectiveness of our method in preserving AI feature representation and distorting relative to conventional metrics. We also compare our method with previously used approaches.

翻译：在安全应用领域中，生物特征认证系统发挥着关键作用，但开发此类系统时常面临隐私与安全的挑战。其中一项基础性难题在于避免直接存储生物特征信息，同时仍能保持较高的识别精度。针对这一问题，我们为人工智能与工程领域做出了贡献。我们提出了一种创新的图像失真技术，该技术能有效使面部图像对人眼不可识别，同时保持神经网络模型对其的可辨识性。从理论角度，我们通过检验不改变预测身份的最大图像失真程度，探讨了当前最先进的生物特征识别神经网络的可靠性。另一方面，应用该技术为解决生物特征认证系统中安全、精度与性能平衡的工程挑战提供了实用方案。通过在广泛使用的数据集上进行实验，我们评估了该方法在保留AI特征表示与基于传统度量标准的失真效果上的有效性，并与先前方法进行了比较。