You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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.

[2]: Over the past decade, text-based CAPTCHA (TBC) have become popular in preventing adversarial attacks and spam in many websites and applications including emails services, social platforms, web-based market places, and recommendation systems. However, in addition to several problems with TBC, it has become increasingly difficult to solve in recent years, to keep up with OCR technologies. Image-based CAPTCHA (IBC), on the other hand, is a relatively new concept that promises to overcome key limitations of TBC. In this paper we present an innovative IBC, DeepCAPTCHA, based on design guidelines, psychological theory and empirical experiments. DeepCAPTCHA exploits the human ability of depth preception. In our IBC users should arrange 3D objects in terms of size (or depth). In our framework for DeepCAPTCHA, we automatically mine 3D models, and use a human-machine Merge Sort algorithm to order these unknown objects. We then create new appearances for these objects at multiplication factor of 200, and present these new images to the end-users for sorting (as CAPTCHA tasks). Humans are able to apply their rapid and reliable object recognition and comparison (arise from years experience with the physical environment) to solve DeepCAPTCHA, while machines are still unable to complete these tasks. Experimental results show that humans can solve DeepCAPTCHA with a high accuracy ( 84 ) and ease, while machines perform dismally.
Image-based CAPTCHAs usually ask the user to recognize an image or to interact with on-screen objects to find a solution. Unlike text-based CAPTCHAs, every image-based design is substantially different from each other. For this reason, a user who faces a CAPTCHA design for the first time needs a little more effort to understand how it works. Studies suggest that image-based CAPTCHAs are more appreciated by users [1] . Indeed, image-based CAPTCHAs usually have a high success rate and they are less challenging than text-based ones [2] . In the following, we report some examples of image-based CAPTCHA that we could group in three sub-categories: static, motion, and interactive.