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Text CAPTCHA Traversal via Knowledge Distillation of Convolutional Neural Networks: Exploring the Impact of Color Channels Selection

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Recent Trends in Analysis of Images, Social Networks and Texts (AIST 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1573))

Abstract

While most of the existing works have investigated the recognition of a fixed-length CAPTCHA, the authors of the article propose to apply knowledge distillation to approximate the predictions of recurrent convolutional neural networks. Such models have proven themselves well in predicting the dynamic length of characters in images. The paper studies the influence of the size and complexity of the training sample on the quality of recognition. The authors studied the effect of individual color channels and their linear combination on the final quality of models. An estimate of the importance of each color channel was acquired using the trainable scalar coefficients in a linear combination. The results obtained made it possible to reduce the size of the input data for recognition without loss in quality of recognition, as well as speed up the training of the model. The analysis of model errors allowed us to offer suggestions for improving ways of countering automatic recognition.

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Authors and Affiliations

  1. Bauman Moscow State Technical University, 105005, Moscow, Russia

    Valery Terekhov, Valery Chernenky & Denis Ishkov

Authors
  1. Valery Terekhov
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  2. Valery Chernenky
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  3. Denis Ishkov
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Corresponding author

Correspondence to Denis Ishkov .

Editor information

Editors and Affiliations

  1. Skolkovo Institute of Science and Technology, Moscow, Russia

    Evgeny Burnaev

  2. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  3. Skolkovo Institute of Science and Technology, Moscow, Russia

    Sergei Ivanov

  4. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

    Andrei Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. Research Computing Center, Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  9. LORIA, Vandœuvre lès Nancy, France

    Amedeo Napoli

  10. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  11. Industrial and Systems Engineering, University of Florida, Gainesville, USA

    Panos M. Pardalos

  12. Aalto University, Espoo, Finland

    Jari Saramäki

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Yandex LLC, Moscow, Russia

    Evgenii Tsymbalov

  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Terekhov, V., Chernenky, V., Ishkov, D. (2022). Text CAPTCHA Traversal via Knowledge Distillation of Convolutional Neural Networks: Exploring the Impact of Color Channels Selection. In: Burnaev, E., et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2021. Communications in Computer and Information Science, vol 1573. Springer, Cham. https://doi.org/10.1007/978-3-031-15168-2_10

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  • DOI: https://doi.org/10.1007/978-3-031-15168-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15167-5

  • Online ISBN: 978-3-031-15168-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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