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

Shiming Wang

Network and Distributed System Security (NDSS) Symposium 2024 · Day 3 · Privacy & ML

In the rapidly evolving landscape of deep learning, the shift towards edge computing for privacy-sensitive data is becoming increasingly prevalent. This talk introduces **Crafter**, a novel system designed to protect user identity from **inversion-based identity theft** in deep learning applications, particularly those processing facial images. Given the growing concerns around data privacy and stringent regulations, pre-processing sensitive raw data at the edge and transmitting only abstract features to the cloud is a critical architectural trend. However, these features, if not adequately protected, can still be inverted by malicious actors to reconstruct original, private images, leading to identity leakage.