论文标题
面对验证旁路
Face Verification Bypass
论文作者
论文摘要
面部验证系统旨在使用特征向量和距离指标来验证声称的身份。但是，没有尝试使用受相同特征向量约束的生成图像绕过这样的系统。在这项工作中，我们培训Stargan V2，以基于人类用户的形式生成多样化的图像，这些图像具有相似的特征向量，但质量上看起来有所不同。然后，我们在自定义面部验证系统上展示了概念证明，并通过在使用相似的面部验证系统的约会应用程序中证明相同的概念证明，并通过在黑匣子设置中证明相同的概念证明。
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.
