Anonymization is a critical problem in the context of face recognition and identification algorithms.
Controversies and ethical concerns have marred the state of the art in face recognition systems.
Counter-manipulation techniques such as blurring and masking have been employed to turn off face recognition, but they alter the image content and are easily detectable.
Adversarial generation and confiscation methods have also been developed, but face recognition algorithms are improving to withstand such attacks.
It highlights its superiority over other methods in system design, production systemization, and evaluation against face recognition Systems.
The authors conclude that anonymization protects against counter-measures such as facial masks and advances in detection by defeating those that rely on denial-of-service attacks.
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