Face Morphing: Fooling a Face Recognition System Is Simple!

TL;DR

This paper presents a simple yet effective face morphing method using a pretrained face recognition model integrated into a GAN, revealing significant vulnerabilities in current face recognition systems to morphed face attacks.

Contribution

The authors introduce a novel face morphing technique that works across diverse scenarios and demonstrate its effectiveness in fooling state-of-the-art face recognition systems.

Findings

Morphed faces achieve high similarity to two identities

Current FR systems only achieve 55-70% accuracy on morphed faces

The method works in unconstrained scenarios

Abstract

State-of-the-art face recognition (FR) approaches have shown remarkable results in predicting whether two faces belong to the same identity, yielding accuracies between 92% and 100% depending on the difficulty of the protocol. However, the accuracy drops substantially when exposed to morphed faces, specifically generated to look similar to two identities. To generate morphed faces, we integrate a simple pretrained FR model into a generative adversarial network (GAN) and modify several loss functions for face morphing. In contrast to previous works, our approach and analyses are not limited to pairs of frontal faces with the same ethnicity and gender. Our qualitative and quantitative results affirm that our approach achieves a seamless change between two faces even in unconstrained scenarios. Despite using features from a simpler FR model for face morphing, we demonstrate that even recent FR systems struggle to distinguish the morphed face from both identities obtaining an accuracy of only 55-70%. Besides, we provide further insights into how knowing the FR system makes it particularly vulnerable to face morphing attacks.