Face Morphing via Adversarial Attack-based Adaptive Blending

Qiaoyun He, Zongyong Deng, Zuyuan He, Qijun Zhao

Abstract: In this paper, we propose an innovative adversarial attack-based adaptive blending architecture (A 3 B) for face morphing attacks. Unlike traditional face morphing methods that evenly blend identity information using a half-half-hard strategy, we propose an approach that considers the varying importance of facial features in different regions of individuals for more precise and clear face morphing. Our method adaptively blends the latent codes of contributing subjects with a weighting mask that assigns different weights to different facial features when blending two faces to fuse their identities. The crux of our method lies in computing the weighting mask given a pair of contributing face images. This is done with the assistance of adversarial attacks, which can adaptively perturb face images to conceal or transfer identity. Owing to the adaptive blending strategy, our proposed approach achieves competitive performance on several state-of-the-art benchmark datasets. In contrast to existing methods, our approach explores the connection between adversarial attacks and morphing attacks for the first time, which generates morphed face images with plausible visual quality and simultaneously preserves the identity of contributing subjects. This novel perspective raises concerns about the potential security risks it poses to current facial recognition systems.