Decoding the animated text-based captchas to verify their robustness against automated attacks

Abstract

In order to protect the web against automated attacks, CAPTCHAs are most widely used mechanism on the internet. Numerous types of CAPTCHAs are introduced due to weaknesses in the earlier designs. Animated CAPTCHAs are one of the design alternatives. Instead of presenting the whole information at once, animated CAPTCHAs present information in various frames over the specific interval of time. As CATPCHAs are ubiquitously used to avoid the serious threats from bots therefore it is important to verify their effectiveness. In this research we have verified their robustness against machine learning attacks. It has been proved that adding the extra time dimension does not necessarily ensure protection against automated attacks. We have attacked the Hello CAPTCHA scheme, which is the most popular animated CAPTCHA scheme available on the internet. By applying novel image processing and machine learning techniques, these CAPTCHAs are decoded with high precision. A pre-trained CNN is used to recognize the extracted characters. In this research, 6 popular types of animated CAPTCHAs along with 41 sub types were successfully deciphered with an overall precision of up to 99.5 %.