A secure annuli CAPTCHA system

Jie Zhang, Min Yen Tsai, Kotcharat Kitchat, Min Te Sun, Kazuya Sakai, Wei Shinn Ku, Thattapon Surasak, Tipajin Thaipisutikul

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Many websites and applications rely on CAPTCHA for protection from bot attacks. Otherwise, users and businesses will be exposed to risks. Although several different CAPTCHA systems have been proposed, the development of deep learning algorithms allows attackers to create more efficient and accurate attack methods. Many studies have shown that existing CAPTCHA systems are no longer safe, especially text-based CAPTCHA. To resolve this issue, a simple, secure, and effective annuli CAPTCHA system is proposed in this paper. In the proposed system, the annuli CAPTCHA image containing the overlapping of circles and ovals is randomly generated. The user wishing to gain access to the system is required to answer correctly the total number of circles and ovals in the image to prove that he/she is not a bot. The security of our proposed CAPTCHA system is verified by three attack methods. Additionally, the usability survey of our CAPTCHA system conducted by anonymous questionnaires shows that our system is user friendly. In other words, the proposed system maintains a high level of usability under the premise of high security. Compared with the existing CAPTCHA system, our CAPTCHA system is significantly better in terms of security, usability and ease of implementation.

Original languageEnglish
Article number103025
JournalComputers and Security
StatePublished - Feb 2023


  • Annuli
  • Deep learning
  • Hough transform
  • Indistinguishable region


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