Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models

Understand

With the increased capabilities at the edge (e.g., mobile device) and more stringent privacy requirement, it becomes a recent trend for deep learning-enabled applications to pre-process sensitive raw data at the edge and transmit the features to the backend cloud for further processing.

• A typical application is to run machine learning (ML) services on facial images collected from different individuals.
• To prevent identity theft, conventional methods commonly rely on an adversarial game-based approach to shed the identity information from the feature.
• However, such methods can not defend against adaptive attacks, in which an attacker takes a countermove against a known defence strategy.