A CAPTCHA DESIGN BASED ON VISUAL REASONING

CAPTCHA is a reverse Turing test to distinguish humans from machines. It is widely used in the internet industry for cyber security. A good CAPTCHA is supposed to be easy for humans but difficult for machines. Many existing CAPTCHA implementations leverage the inability of automatic visual recognition, e.g., recognizing the text or other objects in an image. These CAPTCHAs are becoming more and more vulnerable recently, due to the rapid development of visual recognition techniques. This paper presents our study of using visual reasoning in CAPTCHA design. This CAPTCHA asks the users to find specific object(s) in an image according to a given text query. It is generally easy for humans to understand the text query and make sophisticated reasoning about the image, but still remains difficult and computationally expensive for machines. We describe the CAPTCHA design, provide usability analysis and present security experiments. Moreover, we show that the security can be further improved by the use of neural style transfer.