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A GAN Based Approach to Compare Identical Images with Generative Noise

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Image Analysis and Processing – ICIAP 2022 (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13232))

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Abstract

Generative Adversarial Networks (GAN) have shown impressive results in the generation and translation of images, for instance to generate a painting image with a specific style from a realistic photo. This ability to change of style makes it usable to denoise an image and to the best of our knowledge, GAN has not been used for such applications. This paper presents a generic approach to compare identical images but of which one has been modified by an external process which makes it very noisy to be directly comparable to the original image. This noise results from the process of creating a document and is called generative noise. First, the noisy image is transformed with the generator to get a denoised image more similar to the original one. However, the denoising with the GAN is not perfect due to strong noise processes which requires to transform the original and denoised images into a comparable space, and to check that the quality is good enough to make the comparison. This approach has been applied on the Romanian identity card to compare the identity photo to the ghost image and shows significantly better results than standard comparison approaches.

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Authors and Affiliations

  1. ARIADNEXT - Research Department, Rennes, France

    Damien Simonnet & Ahmad-Montaser Awal

Authors
  1. Damien Simonnet
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  2. Ahmad-Montaser Awal
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Corresponding author

Correspondence to Damien Simonnet .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

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Simonnet, D., Awal, AM. (2022). A GAN Based Approach to Compare Identical Images with Generative Noise. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_19

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  • DOI: https://doi.org/10.1007/978-3-031-06430-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06429-6

  • Online ISBN: 978-3-031-06430-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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