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A systematic literature review: Integrating deep learning models for visual-based CAPTCHA generation

Qian Wang, Shafaf Ibrahim, Zainura Idrus

Abstract


The review conducted a systematic literature review (SLR) of the CAPTCHA generation field to analyze the attack resistance and user-friendliness of the existing CAPTCHA generation techniques. The survey reviewed 28 representative papers out of 1363 CAPTCHA generation-related papers from the Scopus academic database, outlining the image-based CAPTCHA generation techniques. The review employed multiple tables and graphs to assess the resistance to attacks and user-friendliness performance of CAPTCHA. The CAPTCHA is produced by using various generation techniques. The review addresses the following research questions: What deep learning generation techniques are employed for image-based CAPTCHA? Should the evaluation of CAPTCHA generation effectiveness prioritize security alone or consider attack resistance and user-friendliness simultaneously? The answers can provide comprehensive help and future work for scholars. The review further proposes possible future research directions, such as integrating various CAPTCHA generation technologies, including image processing techniques, deep learning algorithms, and human cognitive ability models, to generate more challenging CAPTCHA, effectively preventing attacks by robots or malicious software.


Keywords


CAPTCHA generation; deep learning; anti-recognition; user-friendliness; cybersecurity

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References


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DOI: https://doi.org/10.32629/jai.v7i5.1551

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