question: What is actually happening when you tick the 'I'm a human' box on a captcha? How does it know I'm not a bot?
comment: It uses a lot of information actually. First, it can detect your mouse movements as you go to click the button. If your mouse pointer immediately snaps to the button after the page is loaded then that doesn't seem much like a human. However, if you spend some time reading the page and slowly scroll down to eventually click on the button that is much more like a human. Additionally, it uses information about your browser. What page led you to the page that you are currently viewing? Are there other cookies on your machine that look like normal web browsing traffic? Is your IP address in a range of suspect ones? If the CAPTCHA is uncertain about whether you are a human it will give you a test by having you click on pictures. But, most of the time this is unnecessary. Is it possible to fool this system? Probably. But, it's a constant competition between the developers of the CAPTCHA and people who are trying to fool it.
passages: Software technologies :
  
  BULLET::::- CAPTCHA
  
  BULLET::::- reCAPTCHA
  
  Hardware technologies :
  
  BULLET::::- Radar technology
  
  BULLET::::- Image recognition of human shapes
  
  BULLET::::- Security switch
  
  BULLET::::- Fingerprint sensors
  
  BULLET::::- Infrared detectors
  
  BULLET::::- Acoustic sensors
  
  BULLET::::- Vibration sensors
  
  Section::::Examples.
  
  BULLET::::- reCAPTCHA is a CAPTCHA-like system designed to establish that a computer user is human (normally in order to protect websites from bots) and, at the same time, assist in the digitization of books.
   In 1997 CAPTCHA ("Completely Automated Public Turing test to tell Computers and Humans Apart") was invented. Test is used for detection that computer is operated by human operator ,preventing accessing protected resource by programs , spam robots.
  
  Section::::See also.
  
  BULLET::::- Human sensing
  
  BULLET::::- Captcha
  
  BULLET::::- Security switch
  
  Section::::External links.
  
  Remote human presence detection system Tam, Daniel et al. Assignee:The United States of America, as Represented by the Secretary of the Navy .
  
  Human presence detection  Gear, Gavin et al ;MICROSOFT TECHNOLOGY .
  
  Security Switch Yoffe, Simon; Yoffe, David.
  
  Hardware-based human presence detection Mualem, Avraham; Kupermann, Eli ;Intel Corporation .
   In 2014, reCAPTCHA began implementing behavioral analysis of the browser's interactions with the CAPTCHA to predict whether the user was a human or a bot before displaying the CAPTCHA, and presenting a "considerably more difficult" captcha in cases where it had reason to think the user might be a bot. By end of 2014, this mechanism started to be rolled out to most of the public Google services. 
   BULLET::::- (HC) Computerized tests. A computer generates a problem and presents it to evaluate a user. For example, CAPTCHA tells human users from computer programs by presenting a problem that is supposedly easy for a human and difficult for a computer. While CAPTCHAs are effective security measures for preventing automated abuse of online services, the human effort spent solving them is otherwise wasted. The reCAPTCHA system makes use of these human cycles to help digitize books by presenting words from scanned old books that optical character recognition cannot decipher. (von Ahn et al., 2008).
   BULLET::::- Chew et al. published their work in the 7th International Information Security Conference, ISC'04, proposing three different versions of image recognition CAPTCHAs, and validating the proposal with user studies. It is suggested that one of the versions, the anomaly CAPTCHA, is best with 100% of human users being able to pass an anomaly CAPTCHA with at least 90% probability in 42 seconds.
   During this evaluation, three different automatic tests were given to the principal investigators without human intervention:
  
  BULLET::::1. The large gallery test, which served to baseline how algorithms performed against a database when it has not been properly tuned.
  
  BULLET::::2. The false-alarm test, which tested how well the algorithm monitored an airport for suspected terrorists.
  
  BULLET::::3. The rotation test, which measured how well the algorithm performed when the images of an individual in the gallery had different poses compared to those in the probe set.
   Software that could reverse CAPTCHA with some accuracy by analysing patterns in the generating engine started being developed soon after the creation of CAPTCHA.
  
  In 2013, researchers at Vicarious announced that they had developed a system to solve CAPTCHA challenges from Google, Yahoo!, and PayPal up to 90% of the time.
  
  In 2014, Google engineers demonstrated a system that could defeat CAPTCHA challenges with 99.8% accuracy.
  
  In 2015, Shuman Ghosemajumder, former click fraud czar of Google, stated that there were cybercriminal sites that would defeat CAPTCHA challenges for a fee, to enable various forms of fraud.
   The second team to claim to be the first to invent CAPTCHAs with Luis von Ahn, Manuel Blum, Nicholas J. Hopper, and John Langford, first described CAPTCHAs in a 2003 publication and subsequently received much coverage in the popular press. Their notion of CAPTCHA covers any program that can distinguish humans from computers.
   Section::::History.:Inventorship claims.
   BULLET::::- Podec, a trojan discovered by the security company Kaspersky, forwards CAPTCHA requests to an online human translation service that converts the image to text, fooling the system. Podec targets Android mobile devices.
  
  Section::::Alternative CAPTCHAs schemas.
  
  With the demonstration that text distortion based 
   Note that "CAPTCHA" is an acronym for "Completely Automated Public Turing test to tell Computers and Humans Apart" so that the original designers of the test regard the test as a Turing test to some degree.
  
  Section::::Judgement of sufficient input.
   Section::::Relation to AI.
  
  While used mostly for security reasons, CAPTCHAs also serve as a benchmark task for artificial intelligence technologies. According to an article by Ahn, Blum and Langford, "any program that passes the tests generated by a CAPTCHA can be used to solve a hard unsolved AI problem."
   reCAPTCHA's slogan was "Stop Spam. Read Books." After the introduction of a new version of the reCAPTCHA plugin in 2014, the slogan is now "Easy on Humans, Hard on Bots." A new system featuring image verification was also introduced. In this system, users are asked to just click on a checkbox (the system will verify whether the user is a human or not, for example, with some clues such as already-known cookies or mouse movements within the ReCAPTCHA frame) or, if it fails, select one or more images from a selection of set of images. In 2018, Google started beta testing a completely invisible reCAPTCHA system which does not present any human verification visually. Instead, the new system actively monitors user actions across the entire property and returns a score which represents the probability if it is a human or a bot.
   On October 22, 2013, beating CAPTCHA, Vicarious announced its AI was reliably able to solve modern CAPTCHAs, with character recognition rates of 90% or better. However, Luis von Ahn, a pioneer of early CAPTCHA and founder of reCAPTCHA, expressed skepticism, stating: "It's hard for me to be impressed since I see these every few months." He pointed out that 50 similar claims to that of Vicarious had been made since 2003. Vicarious later published their findings in peer-reviewed journal Science showing they had definitively cracked the popular Turing test.
   The outcome of a bot that appears more human-like than a human player is possibly overstated, since in the tournament in which the bots succeeded, the average 'humanness' rating of the human players was only 41.4%. This showcases some limits of this Turing Test, since the results demonstrate that human behaviour is more complicated and quantitative than was accounted for. In light of this, the BotPrize competition organizers will increase the difficulty in upcoming years with new challenges, forcing competitors to improve their bots.
   In October 2013, artificial intelligence company Vicarious claimed that it had developed a generic CAPTCHA-solving algorithm that was able to solve modern CAPTCHAs with character recognition rates of up to 90%. However, Luis von Ahn, a pioneer of early CAPTCHA and founder of reCAPTCHA, expressed skepticism, stating: "It's hard for me to be impressed since I see these every few months." He pointed out that 50 similar claims to that of Vicarious had been made since 2003.
   On June 27, 2012, Claudia Cruz, Fernando Uceda, and Leobardo Reyes (a group of students from Mexico) published a paper showing a system running on reCAPTCHA images with an accuracy of 82%. The authors have not said if their system can solve recent reCAPTCHA images, although they claim their work to be intelligent OCR and robust to some, if not all changes in the image database.
   CAPTCHA
  
  A CAPTCHA (, an acronym for "completely automated public Turing test to tell computers and humans apart") is a type of challenge–response test used in computing to determine whether or not the user is human.
   BULLET::::- Microsoft (Jeremy Elson, John R. Douceur, Jon Howell, and Jared Saul) claim to have developed Animal Species Image Recognition for Restricting Access (ASIRRA) which ask users to distinguish cats from dogs. Microsoft had a beta version of this for websites to use. They claim "Asirra is easy for users; it can be solved by humans 99.6% of the time in under 30 seconds. Anecdotally, users seemed to find the experience of using Asirra much more enjoyable than a text-based CAPTCHA." This solution was described in a 2007 paper to Proceedings of 14th ACM Conference on Computer and Communications Security (CCS). However, this project was closed in October 2014 and is no longer available.
   In October 2018 at ACM CCS'18 conference, Ye et al. presented a deep learning-based attack that could successfully solve all 11 text captcha schemes used by the top-50 popular website in 2018 with a high success rate. Their work shows that an effective CAPTCHA solver can be trained using as few as 500 real CAPTCHAs, showing that it is possible to quickly launch an attack of a new text CAPTCHA scheme. .
  
  Section::::Circumvention.:Cheap or unwitting human labor.
   They argue that the advantages of using hard AI problems as a means for security are twofold. Either the problem goes unsolved and there remains a reliable method for distinguishing humans from computers, or the problem is solved and a difficult AI problem is resolved along with it. In the case of image and text based CAPTCHAs, if an AI were capable of accurately completing the task without exploiting flaws in a particular CAPTCHA design, then it would have solved the problem of developing an AI that is capable of complex object recognition in scenes.
  
  Section::::Accessibility.
   Section::::Characteristics.
  
  CAPTCHAs are, by definition, fully automated, requiring little human maintenance or intervention to administer, producing benefits in cost and reliability.
  
  The algorithm used to create the CAPTCHA must be made public, though it may be covered by a patent. This is done to demonstrate that breaking it requires the solution to a difficult problem in the field of artificial intelligence (AI) rather than just the discovery of the (secret) algorithm, which could be obtained through reverse engineering or other means.
   Also in March 2017, NtechLabs FindFace algorithm won in the EmotionNet Challenge automatic emotion recognition competition organized by Ohio State University. 37 development teams took part in the competition, and only 2 of them were able to fulfill the conditions in full.
  
  In the fall of 2017, NtechLab won the facial recognition technology competition organized jointly by NIST and IARPA, in two nominations out of three (“Identification Speed” and “Verification Accuracy”).
   During 2015 and 2016 Botlab made available three open source solutions focused on detection and prevention of advertising fraud: 
  
  BULLET::::- Denylist - a blacklist consisting of over 130 million data center IP addresses
  
  BULLET::::- Sitemind - a site research tool for researchers and media planners to counter ad fraud
  
  BULLET::::- Nameles - an entropy based detection and filtering solution to counter ad fraud
  
  Section::::Collaboration with Trade associations.
   CAPTCHA is a form of reverse Turing test. Before being allowed to perform some action on a website, the user is presented with alphanumerical characters in a distorted graphic image and asked to type them out. This is intended to prevent automated systems from being used to abuse the site. The rationale is that software sufficiently sophisticated to read and reproduce the distorted image accurately does not exist (or is not available to the average user), so any system able to do so is likely to be a human.
  