
TL;DR
This paper demonstrates that generative models can produce diverse images with similar feature vectors to a target, effectively bypassing face verification systems in both controlled and real-world scenarios.
Contribution
It introduces a method using StarGAN v2 to generate images that mimic feature vectors of a user, revealing vulnerabilities in face verification systems.
Findings
Generated images can match feature vectors but look different visually.
Face verification systems can be bypassed using generated images.
Vulnerabilities exist in real-world face verification applications.
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFace recognition and analysis · Biometric Identification and Security · Face and Expression Recognition
