Are GAN-based Morphs Threatening Face Recognition?

TL;DR

This study introduces new datasets and tools for face morphing attack detection, revealing that simple landmark-based morphs can be more threatening to face recognition systems than more sophisticated StyleGAN 2 morphs.

Contribution

The paper provides publicly available datasets and code for four morphing attack types, and evaluates their impact on multiple face recognition systems.

Findings

Landmark-based morphs are more effective in fooling face recognition than StyleGAN 2 morphs.

StyleGAN 2 morphs, despite being visually appealing, pose less threat to face recognition systems.

The study highlights the need for improved detection methods for simple morphing attacks.

Abstract

Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in generation of face morphs and their detection is developing rapidly, however very few datasets with morphing attacks and open-source detection toolkits are publicly available. This paper bridges this gap by providing two datasets and the corresponding code for four types of morphing attacks: two that rely on facial landmarks based on OpenCV and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs. We also conduct extensive experiments to assess the vulnerability of four state-of-the-art face recognition systems, including FaceNet, VGG-Face, ArcFace, and ISV. Surprisingly, the experiments demonstrate that, although visually more appealing, morphs based on StyleGAN 2 do not pose a significant threat to the state to face recognition systems, as these morphs were outmatched by the simple morphs that are based facial landmarks.