Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors

Understand

Deep learning-based face recognition (FR) systems pose significant privacy risks by tracking users without their consent.

• While adversarial attacks can protect privacy, they often produce visible artifacts compromising user experience.
• To mitigate this issue, recent facial privacy protection approaches advocate embedding adversarial noise into the natural looking makeup styles.
• However, these methods require training on large-scale makeup datasets that are not always readily available.