Over the last years, most websites on which users can register (e.g., email providers and social networks) adopted CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) as a countermeasure against automated attacks. The battle of wits between designers and attackers of CAPTCHAs led to current ones being annoying and hard to solve for users, while still being vulnerable to automated attacks. In this paper, we propose CAPTCHaStar, a new image-based CAPTCHA that relies on user interaction. This novel CAPTCHA leverages the innate human ability to recognize shapes in a confused environment. We assess the effectiveness of our proposal for the two key aspects for CAPTCHAs, i.e., usability, and resiliency to automated attacks. In particular, we evaluated the usability, carrying out a thorough user study, and we tested the resiliency of our proposal against several types of automated attacks: traditional ones; designed ad-hoc for our proposal; and based on machine learning. Compared to the state of the art, our proposal is more user friendly (e.g., only some 35 of the users prefer current solutions, such as text-based CAPTCHAs) and more resilient to automated attacks.
@cite_5: CAPTCHA is a standard security technology that presents tests to tell computers and humans apart. In this paper, we examine the security of a new CAPTCHA that was deployed until very recently by Megaupload, a leading online storage and delivery website. The security of this scheme relies on a novel segmentation resistance mechanism. However, we show that this CAPTCHA can be segmented using a simple but new automated attack with a success rate of 78 . It takes about 120 ms on average to segment each challenge on a standard desktop computer.
@cite_25: Over the last decade, it has become well-established that a captcha's ability to withstand automated solving lies in the difficulty of segmenting the image into individual characters. The standard approach to solving captchas automatically has been a sequential process wherein a segmentation algorithm splits the image into segments that contain individual characters, followed by a character recognition step that uses machine learning. While this approach has been effective against particular captcha schemes, its generality is limited by the segmentation step, which is hand-crafted to defeat the distortion at hand. No general algorithm is known for the character collapsing anti-segmentation technique used by most prominent real world captcha schemes. This paper introduces a novel approach to solving captchas in a single step that uses machine learning to attack the segmentation and the recognition problems simultaneously. Performing both operations jointly allows our algorithm to exploit information and context that is not available when they are done sequentially. At the same time, it removes the need for any hand-crafted component, making our approach generalize to new captcha schemes where the previous approach can not. We were able to solve all the real world captcha schemes we evaluated accurately enough to consider the scheme insecure in practice, including Yahoo (5.33 ) and ReCaptcha (33.34 ), without any adjustments to the algorithm or its parameters. Our success against the Baidu (38.68 ) and CNN (51.09 ) schemes that use occluding lines as well as character collapsing leads us to believe that our approach is able to defeat occluding lines in an equally general manner. The effectiveness and universality of our results suggests that combining segmentation and recognition is the next evolution of catpcha solving, and that it supersedes the sequential approach used in earlier works. More generally, our approach raises questions about how to develop sufficiently secure captchas in the future.
CAPTCHA designers reacted to these attacks proposing several improvements to mitigate their effectiveness. Some examples follow (between parenthesis we indicate the attack for which the mitigation strategy could be effective): Add more layers of interaction between user and CAPTCHA (could be effective for threat A01 above). Add more distortion to the letters, e.g., warping, scaling, rotating (against A02 and A06). Use of English-like words (for the sake of usability) or totally random words (against A03). Add more pollution to the image, e.g., ticker lines over the letters, confusing background (against A04 and A05). Increment noise, e.g., degrading the quality of the resulting image (against A07). Unfortunately, some of these mitigation strategies have been shown to be ineffective @cite_5 @cite_25 .