MorCode: Face Morphing Attack Generation using Generative Codebooks
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | PN, Aravinda Reddy Ramachandra, Raghavendra Venkatesh, Sushma Rao, Krothapalli Sreenivasa Mitra, Pabitra Krishna, Rakesh |
| author_facet | PN, Aravinda Reddy Ramachandra, Raghavendra Venkatesh, Sushma Rao, Krothapalli Sreenivasa Mitra, Pabitra Krishna, Rakesh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07625 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MorCode: Face Morphing Attack Generation using Generative Codebooks PN, Aravinda Reddy Ramachandra, Raghavendra Venkatesh, Sushma Rao, Krothapalli Sreenivasa Mitra, Pabitra Krishna, Rakesh Computer Vision and Pattern Recognition 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. |
| title | MorCode: Face Morphing Attack Generation using Generative Codebooks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.07625 |