  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.
