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Style matching CAPTCHA: match neural transferred styles to thwart intelligent attacks

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Abstract

Completely automated public turing test to tell computers and humans apart (CAPTCHA) is widely used to prevent malicious automated attacks on various online services. Text- and image-CAPTCHAs have shown broader acceptability due to usability and security factors. However, recent progress in deep learning implies that text-CAPTCHAs can easily be exposed to various fraudulent attacks. Thus, image-CAPTCHAs are getting research attention to enhance usability and security. In this work, the neural-style transfer (NST) is adapted for designing an image-CAPTCHA algorithm to enhance security while maintaining human performance. In NST-rendered image-CAPTCHAs, existing methods inquire a user to identify or localize the salient object (e.g., content) which is solvable effortlessly by off-the-shelf intelligent tools. Contrarily, we propose a Style Matching CAPTCHA (SMC) that asks a user to select the style image which is applied in the NST method. A user can solve a random SMC challenge by understanding the semantic correlation between the content and style output as a cue. The performance in solving SMC is evaluated based on the 1368 responses collected from 152 participants through a web-application. The average solving accuracy in three sessions is 95.61%; and the average response time for each challenge per user is 6.52 s, respectively. Likewise, a Smartphone Application (SMC-App) is devised using the proposed method. The average solving accuracy through SMC-App is 96.33%, and the average solving time is 5.13 s. To evaluate the vulnerability of SMC, deep learning-based attack schemes using Convolutional Neural Networks (CNN), such as ResNet-50 and Inception-v3 are simulated. The average accuracy of attacks considering various studies on SMC using ResNet-50 and Inception-v3 is 37%, which is improved over existing methods. Moreover, in-depth security analysis, experimental insights, and comparative studies imply the suitability of the proposed SMC.

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Notes

  1. https://www.kaggle.com/bryanb/abstract-art-gallery.

  2. https://www.kaggle.com/c/cvdl2020finegrained.

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Acknowledgements

The authors would like to thank the Editors and anonymous reviewers for their valuable comments. The authors are also thankful to the volunteers, experts, and participants who have provided their responses and suggestions for this research.

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  1. Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia, WB, India

    Palash Ray

  2. Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani Campus, Pilani, Rajasthan, India

    Asish Bera

  3. Department of Information Technology, Maulana Abul Kalam Azad University of Technology, Kolkata, WB, India

    Debasis Giri

  4. Department of Computer Science and Engineering, Jadavpur University, Jadavpur, WB, India

    Debotosh Bhattacharjee

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A. conceptualization, methodology, prepared main manuscript. B. conceptualization, methodology, supervision, writing. C. and D. review and editing. A–D. All the authors reviewed the manuscript

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Ray, P., Bera, A., Giri, D. et al. Style matching CAPTCHA: match neural transferred styles to thwart intelligent attacks. Multimedia Systems 29, 1865–1895 (2023). https://doi.org/10.1007/s00530-023-01075-0

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