Skip to main content
SpringerLink
Log in

CAPTCHAs against meddler image identification based on a convolutional neural network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

To strengthen personal identity security through a password, the CAPTCHA authenticates the human user. In this, text-based Hue Icon Montage (HIM) and image-based Hue Icon Montage Click on meddler image and color (HIMC) CAPTCHA are displayed on a panel with a grid of images called a photomontage that contains a set of image groups that each holds six images. Fake facial images (Meddler images) are created by compositing human and animal images called ANTHROMORPH images to create CAPTCHA images against the threat of deep learning. The main contribution to this paper consists of 1) a novel CAPTCHA created with meddler images. 2) this work apply to test whether these images are strong or identified by the machine, the latest manipulated face convolution neural network MANFA framework with Xtreme Gradient Boosting (XGB-MANFA) to overcome the imbalanced dataset problem. 3) A voluminous meddler image dataset (ANTHROMORPH) is created, collected, and validated. This model produces higher security because the success probability rate of HIM and HIMC against brute-force attacks is 3.960 × 10−21. Further, the robustness of meddler images against artificial machine attacks is verified using the XGB-MANFA network, which produces only 26% accuracy that ensures the strength against machine attacks. A user study was conducted to measure the usability of HIM and HIMC CAPTCHAs with 112 participants. The proposed model’s effectiveness, efficiency, learnability, and user satisfaction scores were 0.16, 10.70, 8.40, and 8.62, respectively. The experimental result outperforms well than the existing CAPTCHAs like KCAPTCHA, reCAPTCHA, farett-gender, farett-gender and age.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. ABBYY Fine Reader 14. https://www.abbyy.com. Accessed 18 May 2018

  2. Aleksa A, Blackmer B, Jo C, Nussenbaum M, Tatoris R, Burkett S, Vandenbrink S, Paxton T, PlayThruCAPTCHA, (2011) Accessed 7 January 2018

  3. Basso A, Bergadano F (2010) Anti-bot strategies based on human interactive proofs. In: Handbook of information and communication security, 273–291. https://doi.org/10.1007/978-3-642-04117-4_15

    Chapter  Google Scholar 

  4. Betaface API 2018. https://www.betafaceapi.com. Accessed 12 June 2018.

  5. Bigham JP, Cavender AC (2009) Evaluating existing audio CAPTCHAs and an interface optimised for non-visual use. In: Proceedings of the 27th international conference on human factors in computing systems - CHI 09. https://doi.org/10.1145/1518701.1518983

    Chapter  Google Scholar 

  6. Bursztein E, Bethard S, Fabry C, Mitchell JC, Jurafsky D (2010) How good are humans at solving CAPTCHAs? A large scale evaluation. In: 2010 IEEE symposium on security and privacy. https://doi.org/10.1109/sp.2010.31

    Chapter  Google Scholar 

  7. Bursztein E, Martin M, Mitchell J (2011) Text-based CAPTCHA strengths and weaknesses. In: Proceedings of the 18th ACM conference on computer and communications security - CCS ‘11. https://doi.org/10.1145/2046707.2046724

    Chapter  Google Scholar 

  8. ChangeFaces application 2018. https://www.changefaces.com. Accessed 20 May 2018

  9. Chaudhari SK, Deshpande AR, Bendale SB, Kotian RV (2011) 3D drag-n-drop CAPTCHA enhanced security through CAPTCHA. In: Proceedings of the international conference & workshop on emerging trends in technology - ICWET ‘11. https://doi.org/10.1145/1980022.1980153

    Chapter  Google Scholar 

  10. Chellapilla K, Larson K, Simard P, Czerwinski M (2005) Designing human friendly human interaction proofs (HIPs). In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/1054972.1055070

    Chapter  Google Scholar 

  11. Chewa M, Baird HS (2003) BaffleText: a human interactive proof. In: Proceedings of the SPIE/IS and T document Recognition and retrieval, Santa Clara, CA. https://doi.org/10.1117/12.479682

    Chapter  Google Scholar 

  12. Chithra PL, Sathya K (2021) Scanning-to-speech challenge-response authentication test for visually impaired. Comput Electr Eng 92:107133. https://doi.org/10.1016/j.compeleceng.2021.107133

    Article  Google Scholar 

  13. Crimeware: Bots, Symantec Corporation, 2014. [Online]. Accessed 10 July 2018

  14. Dang LM, Hassan SI, Im S, Moon H (2019) Face image manipulation detection based on a convolutional neural network. Expert Syst Appl 129:156–168. https://doi.org/10.1016/j.eswa.2019.04.005

    Article  Google Scholar 

  15. Face Recognition API 2018. https://www.kairos.com. Kairos. Accessed 10 April 2018

  16. Gao H, Wang W, Qi J, Wang X, Liu X, Yan J (2013) The robustness of hollow CAPTCHAs. In: Proceedings of the 2013 ACM SIGSAC conference on Computer & Communications Security - CCS ‘13. https://doi.org/10.1145/2508859.2516732

    Chapter  Google Scholar 

  17. Guerar M, Merlo A, Migliardi M, Palmieri F (2018) Invisible CAPPCHA: a usable mechanism to distinguish between malware and humans on the mobile IoT. Computers & Security 78:255–266. https://doi.org/10.1016/j.cose.2018.06.007

    Article  Google Scholar 

  18. Hernández-Castro CJ, Li S, R-Moreno MD (2020) All about uncertainties and traps: statistical oracle-based attacks on a new CAPTCHA protection against oracle attacks. Comput Secur 92:101758. https://doi.org/10.1016/j.cose.2020.101758

    Article  Google Scholar 

  19. Hidalgo JMG, Alvarez G (2011) CAPTCHAs. Adv Comput:109–181. https://doi.org/10.1016/b978-0-12-385510-7.00003-5

  20. http://www.eyedea.cz.Eyedea. Accessed 2 February 2018

  21. Hwang K-F, Huang C-C, You G-N (2012) A spelling based CAPTCHA system by using click. In: 2012 international symposium on biometrics and security technologies. https://doi.org/10.1109/isbast.2012.17

    Chapter  Google Scholar 

  22. Ince IF, Salman YB, Yildirim ME, Yang T-C (2009) Execution time prediction for 3D interactive CAPTCHA by keystroke level model. In: 2009 fourth international conference on computer sciences and convergence information technology. https://doi.org/10.1109/iccit.2009.105

    Chapter  Google Scholar 

  23. Liao CJ, Yang CJ, Yang JT, Hsu HY, Liu JW et al (2013) A Game and Accelerometer-based CAPTCHA Scheme for Mobile Learning System. In: Jan H et al (eds) Proceedings of the world conference on educational multimedia, hypermedia and telecommunications, pp 1385–1390

    Google Scholar 

  24. Lupkowski P, Urbanski M (2008) SemCAPTCHAuser-friendly alternative for OCR-based CAPTCHA systems. In: 2008 international multiconference on computer science and information technology. https://doi.org/10.1109/imcsit.2008.4747260

    Chapter  Google Scholar 

  25. Microsoft Azure 2018. https://azure.microsoft.com.

  26. New OCR tool 2018. https://www.newocr.com. Accessed 8 March 2018

  27. I. Nikiforov, M. Vladykin, N. Price, Wordpress KeyCAPTCHA, 2010. Accessed 5 January 2018

  28. Online OCR Space tool. https://ocr.space;2018.OCRSpace2018. Accessed 1 March 2018

  29. Online OCR tool. https://www.onlineocr.net;2018. Accessed 14 May 2018

  30. Ouyang Z, Zhai X, Wu J, Yang J, Yue D, Dou C, Zhang T (2021) A cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble. Computers & Security 103:102178. https://doi.org/10.1016/j.cose.2021.102178

    Article  Google Scholar 

  31. Roshanbin N, Miller J (2013) A survey and analysis of current CAPTCHA approaches. J web Eng 12(1):1–40

    Google Scholar 

  32. Schryen G, Wagner G, Schlegel A (2016) Development of two novel face-recognition CAPTCHAs: a security and usability study. Computers & Security 60:95–116. https://doi.org/10.1016/j.cose.2016.03.007

    Article  Google Scholar 

  33. Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. https://doi.org/10.1201/9781420036268

    Book  MATH  Google Scholar 

  34. Shi C, Ji S, Liu Q, Liu C, Chen Y, He Y, … Wang T (2020) Text captcha is dead? A large scale deployment and empirical study. In: Proceedings of the 2020 ACM SIGSAC conference on computer and communications security. https://doi.org/10.1145/3372297.3417258

    Chapter  Google Scholar 

  35. SmallSeotools 2018. https://smallseotools.com. Accessed 16 May 2018

  36. Von Ahn L, Blum M, Langford J (2004) Telling humans and computers apart automatically. Commun ACM 47(2):56–60. https://doi.org/10.1145/966389.966390

    Article  Google Scholar 

  37. Wang J, Qin J, Xiang X, Tan Y, Pan N (2019) CAPTCHA recognition based on deep convolutional neural network. Math Biosci Eng 16(5):5851–5861. https://doi.org/10.3934/mbe.2019292

    Article  MathSciNet  Google Scholar 

  38. Yan J, El Ahmad AS (2008) A low-cost attack on a Microsoft captcha. Proceedings of the 15th ACM conference on computer and communications security - CCS ‘08. https://doi.org/10.1145/1455770.1455839

  39. Zhang X, Liu X, Sarkodie-Gyan T, Li Z (2021) Development of a character CAPTCHA recognition system for the visually impaired community using deep learning. Machine Vision and Applications 32(1). https://doi.org/10.1007/s00138-020-01160-8

Download references

Acknowledgments

We would like to thank all the participants who contributed to carry out the experimental study successfully. This work has been funded by the University of Madras (India) under University Research Fellow Grant No; GCCO/URF/Comp. Science/2019-20/323.

Availability of data and material

Not applicable.

Code availability

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Madras, Chennai, TN, India

    P. L. Chithra & K. Sathya

Authors
  1. P. L. Chithra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Sathya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. L. Chithra.

Ethics declarations

Not applicable.

Conflict of interest

This manuscript’s authors note that no conflict of interest exists.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chithra, P.L., Sathya, K. CAPTCHAs against meddler image identification based on a convolutional neural network. Multimed Tools Appl 81, 8633–8652 (2022). https://doi.org/10.1007/s11042-022-11961-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-11961-w

Keywords

Search

Navigation