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_19: With the growing use of Internet and its services, a large number of organizations are making use of it to provide and seek information of the people using those services. This has raised the chances of attacks on such services by interrupting them sending multiple requests to the servers providing these services programmatically. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) provides a way to differentiate users into humans and bots, or computer programs. This paper includes a new technique that utilizes image from custom mouse cursors and outperforms some most popular CAPTCHA techniques such as Text - based CAPTCHAs and previous Image - based CAPTCHAs.
@cite_15: Most commonly used CAPTCHAs are text-based CAPTCHAs which relay on the distortion of texts in the background image. With the development of automated computer vision techniques, which have been designed to remove noise and segment the distorted strings to make characters readable for OCR, traditional text-based CAPTHCAs are not considered safe anymore for authentication. A novel image based CAPTCHA which involves in solving a jigsaw puzzle is presented in this paper. An image is divided into an niAn (n=3, 4 or 5, depends on security level) pieces to construct the jigsaw puzzle CAPTCHA. Only two of the pieces are misplaced from their original positions. Users are required to find the two pieces and swap them. Considering the previous works which are devoted to solving jigsaw puzzles using edge matching technique, the edges of all pieces are processed with glitch treatment to prevent the computer automatic solving. Experiments and security analysis proved that human users can complete the CAPTCHA verification quickly and accurately, but computers rarely can. It is a promising substitution to the current text-based CAPTCHA.
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@cite_23: The most common CAPTCHA technique, Gimpy which requires a user to type in twisted characters has following problems. Characters are over twisted because some spammer uses OCR to bypass verification. Type in operation on mobile and tablet devices is difficult where keyboard are not utilized. Our method uses multiple noise images where invisible objects or message is hidden. The hidden objects appear when two images are overlapped at certain position. Finding the point is a key to pass verification which is difficult for none human intelligence. This enhances usability on tablet devices since a user only has to use one finger to get verified instead of using a keyboard.
Interactive CAPTCHAs mitigate the relay attack threat. For example, Noise CAPTCHA @cite_23 presents a transparent noisy image overlapped to a noisy background. The user needs to drag this image until he can recognize a well formed text. Cursor CAPTCHA @cite_19 changes the appearance of mouse cursor into another random object. The user needs to overlap the cursor on the identical object placed in a random generated image. Jigsaw CAPTCHA @cite_15 reprises the classical jigsaw puzzle. Indeed, the user needs to correctly rearrange the pieces of a jigsaw. Finally, PlayThru @cite_3 asks the user to solve a randomly generated mini-game. These mini-games require to drag objects on their correct spots.