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For a long time now, computers and humans have been separated using fully automated public turing tests, or CAPTCHAs. There are several variations of CAPTCHAs, including text-, picture-, audio-, video-, and math-based ones. To remain current, the CAPTCHA tests have undergone continuous updating. In order to work around the limitations of most traditional techniques, the stated model employs a flexible CAPTCHA creation process. The sophistication of bot traffic is rising, and conventional CAPTCHA techniques are finding it difficult to keep users satisfied while offering sufficient protection. In the first tier of the proposed model, there is an interactive Scratch Card CAPTCHA that engages users with an entertaining and user-friendly interface. A CAPTCHA for image recognition follows, which is meant to test users’ ability to complete visual tasks that are more challenging for machines to understand. In order to improve detection accuracy, the system makes use of machine learning models such as SVM (support vector machine), CNNs (convolutional neural networks), and RNN (recurrent neural network) that examine patterns of user engagement. This enables ongoing learning and modification depending on information gathered. Furthermore, concepts based on user experience are integrated to guarantee that the CAPTCHA procedure is still easy to use and captivating. Higher security metrics, better user interface, and experience with higher bot detection rates are anticipated results. By successfully thwarting illegal access attempts, this novel method not only seeks to make the Internet a safer place, but also guarantees a smooth and pleasurable experience for authorized users. The continued development and refining of this technology underline its potential to dramatically boost online security in an era of sophisticated automated attacks. In terms of security and usability, our method performs better than conventional CAPTCHA systems. With a 94.5% accuracy rate, the CNN model has considerable potential, and more refinement may increase the model’s resistance to hostile attacks.

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Smart CAPTCHA: Two-Layered CAPTCHA System with Scratch Card-Enhanced User Interface

  • 会议论文

  • 发表日期：

    2025年

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  • R. Sanjai Kumar,
  • J. Padmapriya,
  • S. Murugavalli

摘要

For a long time now, computers and humans have been separated using fully automated public turing tests, or CAPTCHAs. There are several variations of CAPTCHAs, including text-, picture-, audio-, video-, and math-based ones. To remain current, the CAPTCHA tests have undergone continuous updating. In order to work around the limitations of most traditional techniques, the stated model employs a flexible CAPTCHA creation process. The sophistication of bot traffic is rising, and conventional CAPTCHA techniques are finding it difficult to keep users satisfied while offering sufficient protection. In the first tier of the proposed model, there is an interactive Scratch Card CAPTCHA that engages users with an entertaining and user-friendly interface. A CAPTCHA for image recognition follows, which is meant to test users’ ability to complete visual tasks that are more challenging for machines to understand. In order to improve detection accuracy, the system makes use of machine learning models such as SVM (support vector machine), CNNs (convolutional neural networks), and RNN (recurrent neural network) that examine patterns of user engagement. This enables ongoing learning and modification depending on information gathered. Furthermore, concepts based on user experience are integrated to guarantee that the CAPTCHA procedure is still easy to use and captivating. Higher security metrics, better user interface, and experience with higher bot detection rates are anticipated results. By successfully thwarting illegal access attempts, this novel method not only seeks to make the Internet a safer place, but also guarantees a smooth and pleasurable experience for authorized users. The continued development and refining of this technology underline its potential to dramatically boost online security in an era of sophisticated automated attacks. In terms of security and usability, our method performs better than conventional CAPTCHA systems. With a 94.5% accuracy rate, the CNN model has considerable potential, and more refinement may increase the model’s resistance to hostile attacks.