MorCode: Face Morphing Attack Generation using Generative Codebooks

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Aravinda Reddy PN, Ramachandra, Raghavendra, Venkatesh, Sushma, Krothapalli Sreenivasa Rao, Mitra, Pabitra, Krishna, Rakesh
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.10.2024
Subjects
Digital imaging
Encoders-Decoders
Face recognition
Generative adversarial networks
Generative artificial intelligence
Image quality
Morphing
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Summary: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 \textit{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, \textit{MorCode}, was benchmarked using three different face recognition systems. The obtained results indicate the highest attack potential of the proposed \textit{MorCode} when compared with five state-of-the-art morphing generation methods on both digital and print scan data.
ISSN:2331-8422