Face Verification Bypass
This work addresses security vulnerabilities in face verification for applications like dating apps, but it is incremental as it builds on existing generative methods.
The paper tackled the problem of bypassing face verification systems by generating diverse images with similar feature vectors, and demonstrated a proof of concept that successfully bypassed custom and black-box systems.
Face verification systems aim to validate the claimed identity using feature vectors and distance metrics. However, no attempt has been made to bypass such a system using generated images that are constrained by the same feature vectors. In this work, we train StarGAN v2 to generate diverse images based on a human user, that have similar feature vectors yet qualitatively look different. We then demonstrate a proof of concept on a custom face verification system and verify our claims by demonstrating the same proof of concept in a black box setting on dating applications that utilize similar face verification systems.