  Face morphing attacks circumvent face recognition systems (FRSs) by creating
a morphed image that contains multiple identities. However, existing face
morphing attack methods either sacrifice image quality or compromise the
identity preservation capability. Consequently, these attacks fail to bypass
FRSs verification well while still managing to deceive human observers. These
methods typically rely on global information from contributing images, ignoring
the detailed information from effective facial regions. To address the above
issues, we propose a novel morphing attack method to improve the quality of
morphed images and better preserve the contributing identities. Our proposed
method leverages the hierarchical generative network to capture both local
detailed and global consistency information. Additionally, a mask-guided image
blending module is dedicated to removing artifacts from areas outside the face
to improve the image's visual quality. The proposed attack method is compared
to state-of-the-art methods on three public datasets in terms of FRSs'
vulnerability, attack detectability, and image quality. The results show our
method's potential threat of deceiving FRSs while being capable of passing
multiple morphing attack detection (MAD) scenarios.
