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]: 

[2]: Some Internet Web sites for providing services to their customers, ask them to register in the Web site. Unfortunately hackers write programs to make automatic false enrollments which waste the Web site's resources. To solve this problem, systems known as CAPTCHA (completely automated public Turing test to tell computers and humans apart) have been introduced to distinguish between human user and computer program. One of the CAPTCHA methods is collage CAPTCHA. In this method some shapes are shown with distortion and the user is asked to choose a specific object. In this paper we increase the resistance of this method to attacks. For this purpose, we also show some other objects on the right of the screen. Some of these objects are same as the previous objects, but have the different shapes. Now we ask the user to choose a specific object and also its same object on the right of the screen. The user will be passed the test if he chooses the two similar objects correctly. In this method because the computer program should also recognize the similar object, the possibility of passing the test by computer is more reduced.

[3]: We present a technique for using content-based video labeling as a CAPTCHA task. Our CAPTCHAs are generated from YouTube videos, which contain labels (tags) supplied by the person that uploaded the video. They are graded using a video's tags, as well as tags from related videos. In a user study involving 184 participants, we were able to increase the human success rate on our video CAPTCHA from roughly 70 to 90 , while keeping the success rate of a tag frequency-based attack fixed at around 13 . Through a different parameterization of the challenge generation and grading algorithms, we were able to reduce the success rate of the same attack to 2 , while still increasing the human success rate from 70 to 75 . The usability and security of our video CAPTCHA appears to be comparable to existing CAPTCHAs, and a majority of participants (60 ) indicated that they found the video CAPTCHAs more enjoyable than traditional CAPTCHAs in which distorted text must be transcribed.

[4]: We present Asirra (Figure 1), a CAPTCHA that asks users to identify cats out of a set of 12 photographs of both cats and dogs. Asirra is easy for users; user studies indicate it can be solved by humans 99.6 of the time in under 30 seconds. Barring a major advance in machine vision, we expect computers will have no better than a 1 54,000 chance of solving it. Asirra’s image database is provided by a novel, mutually beneficial partnership with Petfinder.com. In exchange for the use of their three million images, we display an “adopt me” link beneath each one, promoting Petfinder’s primary mission of finding homes for homeless animals. We describe the design of Asirra, discuss threats to its security, and report early deployment experiences. We also describe two novel algorithms for amplifying the skill gap between humans and computers that can be used on many existing CAPTCHAs.

[5]: 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.
One of the representative static image-based CAPTCHAs was Asirra [4] , which was discontinued in fall 2014. Asirra asks the user to distinguish between cats and dogs, on twelve different photos randomly taken from an external website. Another static image-based CAPTCHA is Collage [2] : it requests to click on a specific picture, among six pictures randomly taken. Deep CAPTCHA [5] prompts the user with six 3D models of real world objects and it asks to sort them by their size. Some designers focus on CAPTCHA that requires video recognition rather than static image recognition. For example, Motion CAPTCHA [1] shows the user a randomly chosen video from a database, then it asks the user to identify the action performed by the person in the video. Similarly, YouTube Videos CAPTCHA [3] leverages on real video in YouTube service, and it asks the user to write three tags related to the content of the video.