Adversarial Text-Based CAPTCHA Generation Method Utilizing Spatial Smoothing

Yuichiro Matsuura, Hiroya Kato, Iwao Sasase

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


The development of deep learning (DL) techniques has enabled to crack traditional text-based CAPTCHA (Com-pletely Automated Public Turing test to tell Computers and Humans Apart), which results in new security issues. As a coun-termeasure against DL based attacks, the adversarial CAPTCHA is well suited since it can increase the difficulty of machine recognition while ensuring human readability. However, spatial smoothing can negate the effectiveness of adversarial CAPTCHAs because adversarial noises in them are subject to averaging pixels. As far as we know, there are no effective counters against spatial smoothing, whereas it is the critical problem which facilitates spreading automated attacks. Therefore, to address the unsolved problem, in this paper, we propose an adversarial text-based CAPTCHA generation method utilizing spatial smoothing. We focus on the fact that when spatial smoothing is applied to an image, the amount of information it carries decreases, making the whole image blurred. Spatial smoothing is only viable as an attack when the mitigation of the adversarial noise has a larger impact than the whole image getting blurred. Thus, when the degree of spatial smoothing exceeds a certain threshold, the impact of the two aspects reverses, and the difficulty of the recognition increase. By utilizing this phenomenon in the generation of CAPTCHAs, the proposed method can indirectly neutralize the intended effect of spatial smoothing by attackers, preventing the recognition rate from increasing. Our evaluation shows the proposed method can reduce the recognition rate by up to 34%, compared to the conventional method. Besides, an experiment on human recognition rates marked 73.67%, showing that human recognition is maintained at an acceptable level.

Original languageEnglish
Title of host publication2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728181042
Publication statusPublished - 2021
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: 2021 Dec 72021 Dec 11

Publication series

Name2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings


Conference2021 IEEE Global Communications Conference, GLOBECOM 2021


  • adversarial example
  • deep neural network
  • spatial smoothing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics


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