Year: 2022
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Sarda, Sanjana
Institution: Stanford University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4226424425