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Gotta CAPTCHA ’Em All: A Survey of 20 Years of the Human-or-computer Dilemma

Authors:

Luca Verderame,

Mauro Migliardi,

Francesco Palmieri,

Alessio MerloAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 9

Article No.: 192, Pages 1 - 33

https://doi.org/10.1145/3477142

Published: 08 October 2021 Publication History

Abstract

A recent study has found that malicious bots generated nearly a quarter of overall website traffic in 2019 [102]. These malicious bots perform activities such as price and content scraping, account creation and takeover, credit card fraud, denial of service, and so on. Thus, they represent a serious threat to all businesses in general, but are especially troublesome for e-commerce, travel, and financial services. One of the most common defense mechanisms against bots abusing online services is the introduction of Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), so it is extremely important to understand which CAPTCHA schemes have been designed and their actual effectiveness against the ever-evolving bots. To this end, this work provides an overview of the current state-of-the-art in the field of CAPTCHA schemes and defines a new classification that includes all the emerging schemes. In addition, for each identified CAPTCHA category, the most successful attack methods are summarized by also describing how CAPTCHA schemes evolved to resist bot attacks, and discussing the limitations of different CAPTCHA schemes from the security, usability, and compatibility point of view. Finally, an assessment of the open issues, challenges, and opportunities for further study is provided, paving the road toward the design of the next-generation secure and user-friendly CAPTCHA schemes.

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Cited By

Rodriguez COppenheimer D(2024)Creating a Bot-tleneck for malicious AI: Psychological methods for bot detectionBehavior Research Methods10.3758/s13428-024-02357-956:6(6258-6275)Online publication date: 1-Apr-2024
https://doi.org/10.3758/s13428-024-02357-9
Wan XJohari JRuslan F(2024)Variational Color Shift and Auto-Encoder Based on Large Separable Kernel Attention for Enhanced Text CAPTCHA Vulnerability AssessmentInformation10.3390/info1511071715:11(717)Online publication date: 7-Nov-2024
https://doi.org/10.3390/info15110717
Pinzón NKoundinya VGalt RDowling WBaukloh MTaku-Forchu NSchohr TRoche LIkendi SCooper MParker LPathak T(2024)AI-powered fraud and the erosion of online survey integrity: an analysis of 31 fraud detection strategiesFrontiers in Research Metrics and Analytics10.3389/frma.2024.14327749Online publication date: 2-Dec-2024
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Index Terms

Gotta CAPTCHA ’Em All: A Survey of 20 Years of the Human-or-computer Dilemma
1. Security and privacy
  1. Security services
    1. Authentication
      1. Graphical / visual passwords

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 9

December 2022

800 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3485140

Editor:
Albert Zomaya
University of Sydney, Australia

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 08 October 2021

Accepted: 01 July 2021

Revised: 01 May 2021

Received: 01 March 2021

Published in CSUR Volume 54, Issue 9

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Cited By

Rodriguez COppenheimer D(2024)Creating a Bot-tleneck for malicious AI: Psychological methods for bot detectionBehavior Research Methods10.3758/s13428-024-02357-956:6(6258-6275)Online publication date: 1-Apr-2024
https://doi.org/10.3758/s13428-024-02357-9
Wan XJohari JRuslan F(2024)Variational Color Shift and Auto-Encoder Based on Large Separable Kernel Attention for Enhanced Text CAPTCHA Vulnerability AssessmentInformation10.3390/info1511071715:11(717)Online publication date: 7-Nov-2024
https://doi.org/10.3390/info15110717
Pinzón NKoundinya VGalt RDowling WBaukloh MTaku-Forchu NSchohr TRoche LIkendi SCooper MParker LPathak T(2024)AI-powered fraud and the erosion of online survey integrity: an analysis of 31 fraud detection strategiesFrontiers in Research Metrics and Analytics10.3389/frma.2024.14327749Online publication date: 2-Dec-2024
https://doi.org/10.3389/frma.2024.1432774
Manivannan ASethuraman SVimala Sudhakaran D(2024)mCaptcha: Replacing Captchas with Rate limiters to Improve Security and AccessibilityCommunications of the ACM10.1145/366062867:10(70-80)Online publication date: 26-Sep-2024
https://dl.acm.org/doi/10.1145/3660628
Ousat BSchafir EHoang DTofighi MNguyen CArshad SUluagac SKharraz AChua TNgo CKa-Wei Lee RKumar RLauw H(2024)The Matter of Captchas: An Analysis of a Brittle Security Feature on the Modern WebProceedings of the ACM Web Conference 202410.1145/3589334.3645619(1835-1846)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645619
Ji TLuo YLin YYang YZheng QLian SLi J(2024) ImageVeriBypasser : An image verification code recognition approach based on Convolutional Neural Network Expert Systems10.1111/exsy.13658Online publication date: 25-Jun-2024
https://doi.org/10.1111/exsy.13658
Duan FWang DJia C(2024)A Security Analysis of Honey Vaults2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00219(1424-1442)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00219
Lajmi HIdoudi FNjah HKammoun HNjah I(2024)Strengthening Applications' Security with Handwritten Arabic Calligraphy Captcha2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)10.1109/RTSI61910.2024.10761297(548-553)Online publication date: 18-Sep-2024
https://doi.org/10.1109/RTSI61910.2024.10761297
Nguyen THoang V(2024)Recognition of CAPTCHAs Utilizing The Middle Part of The Convolutional Feature Map2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)10.1109/MCSoC64144.2024.00073(407-412)Online publication date: 16-Dec-2024
https://doi.org/10.1109/MCSoC64144.2024.00073
Kopperundevi NSaiTejeswarreddy PRevanth MGautham M(2024)Captcha Recognition Using CNN2024 4th Asian Conference on Innovation in Technology (ASIANCON)10.1109/ASIANCON62057.2024.10838000(1-5)Online publication date: 23-Aug-2024
https://doi.org/10.1109/ASIANCON62057.2024.10838000
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