In: Proceedings of International Conference on Information Science and Applications (ICISA), pp. Springer, Heidelberg (2003)Īlmazyad, A., Ahmad, Y., Kouchay, S.: Multi-modal captcha: A user verification scheme. Von Ahn, L., Blum, M., Hopper, N.J., Langford, J.: CAPTCHA: Using Hard AI Problems for Security. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. These experiments suggest that this high performance is caused more by biases in the facial datasets used by Avatar Captchas and not by a fundamental flaw in the concept itself, but nevertheless our results highlight the difficulty in creating Captcha tasks that are immune to automatic solution. Our results show that using these techniques, a bot can successfully solve Avatar Captchas as often as humans can. We test a variety of modern object recognition and machine learning approaches on the problem of avatar versus human face classification. Avatar Captchas are a recently-proposed variant in which users are asked to classify between human faces and computer-generated avatar faces, and have been shown to be secure if bots employ random guessing. Captchas are challenge-response tests used in many online systems to prevent attacks by automated bots.
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