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Multimedia Systems

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30.03.2023 | Regular Paper

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

verfasst von: Palash Ray, Asish Bera, Debasis Giri, Debotosh Bhattacharjee

Erschienen in: Multimedia Systems | Ausgabe 4/2023

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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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Titel: Style matching CAPTCHA: match neural transferred styles to thwart intelligent attacks
verfasst von: Palash Ray
Asish Bera
Debasis Giri
Debotosh Bhattacharjee
Publikationsdatum: 30.03.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Multimedia Systems / Ausgabe 4/2023
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI: https://doi.org/10.1007/s00530-023-01075-0

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