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An Experimental Investigation of Text-based CAPTCHA Attacks and Their Robustness

Published: 16 January 2023 Publication History

Abstract

Text-based CAPTCHA has become one of the most popular methods for preventing bot attacks. With the rapid development of deep learning techniques, many new methods to break text-based CAPTCHAs have been developed in recent years. However, a holistic and uniform investigation and comparison of these attacks’ effects is lacking due to inconsistent choices of model structures, training datasets, and evaluation metrics. In this article, we perform an experimental investigation on the effects of existing attacks on text-based CAPTCHA schemes. We first summarize existing text-based CAPTCHAs using a newly proposed taxonomy based on their resistance mechanisms and systematically review corresponding attacks in terms of methods and pros/cons. Then, we introduce a unified attack framework that contains a number of different attack modules and transfer learning strategies. Applying this framework, we extensively evaluate the performance of known attacks on 20 CAPTCHA schemes in terms of accuracy and efficiency; then, we investigate the robustness of these widely used schemes and discover the effects of previously unexplored attacks. Finally, we discuss future CAPTCHA designs based on our experimental results and findings. Our work also contributes to the CAPTCHA community by offering an open-access dataset that contains 22 different CAPTCHA sample sets.

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      cover image ACM Computing Surveys
      ACM Computing Surveys  Volume 55, Issue 9
      September 2023
      835 pages
      ISSN:0360-0300
      EISSN:1557-7341
      DOI:10.1145/3567474
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      Publication History

      Published: 16 January 2023
      Online AM: 31 August 2022
      Accepted: 23 August 2022
      Revised: 18 August 2022
      Received: 15 January 2022
      Published in CSUR Volume 55, Issue 9

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      1. Text-based CAPTCHA
      2. deep learning
      3. attack
      4. robustness study

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      • Natural Science Foundation of China
      • Zhejiang Lab
      • Zhejiang Lab’s International Talent Fund for Young Professionals
      • Academy of Finland

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