2024/10/10 by Aravinda Reddy PN, PN, Aravinda Reddy, Raghavendra Ramachandra +9 · 1 citation
Computer Science · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis #Social Robot Interaction and HRI
paper · pdf · doi:10.48550/arxiv.2410.07625
openalex publication_date 2024/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Face recognition systems (FRS) can be compromised by face morphing attacks, which blend textural and geometric information from multiple facial images. The rapid evolution of generative AI, especially Generative Adversarial Networks (GAN) or Diffusion models, where encoded images are interpolated to generate high-quality face morphing images. In this work, we present a novel method for the automatic face morphing generation method MorCode, which leverages a contemporary encoder-decoder architecture conditioned on codebook learning to generate high-quality morphing images. Extensive experiments were performed on the newly constructed morphing dataset using five state-of-the-art morphing generation techniques using both digital and print-scan data. The attack potential of the proposed morphing generation technique, MorCode, was benchmarked using three different face recognition systems. The obtained results indicate the highest attack potential of the proposed MorCode when compared with five state-of-the-art morphing generation methods on both digital and print scan data.