  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.
