Challenge of Deep Learning against CAPTCHA with Amodal Completion and Aftereffects by Colors
Tomoka Azakami, Chihiro Shibata, Ryuya Uda · 2016
We make experiments of machine learning for our CAPTCHA proposed as an effective CAPTCHA with amodal completion and aftereffects by colors. CAPTCHA is a method that distinguishes human beings from artificial intelligence in order to prohibit malicious programs from acquiring accounts on Internet. The most popular CAPTCHA is text-based CAPTCHA with distorted alphabets and numbers. However, it is known that all of text-based CAPTCHA algorithms can be analyzed by computers. In addition, too much distortion or noise prevents human beings from recognizing alphabets or numbers. As a solution of the problems, an effective text-based CAPTCHA algorithm with amodal completion was proposed by our team. Our CAPTCHA causes computers a large amount of calculation costs while amodal completion helps human beings to recognize characters momentarily. Our CAPTCHA has evolved with aftereffects and combinations of complementary colors. In this paper, we evaluate our CAPTCHA by machine learning since machine learning is faster and more accurate than existing calculations by a computer. We confirm the limitations of machine learning. Especially, we focus on whether a computer can recognize characters without knowledge of amodal completion.