07 Nov 2019 • 10 min read
07 Nov 2019 • 10 min read
CAPTCHA, an acronym for “Completely Automated Public Turing test to tell Computers and Humans Apart.”, is a module used in websites, mobile apps, and APIs to distinguish automated computer programs from genuine human users.
During the early days of the Internet, traditional CAPTCHAs helped mitigate bot threats from automated attacks. They are widely used to fight malicious bot attacks, such as account takeovers, credential stuffing, and more.
However, cybersecurity is a cat-and-mouse game between attackers and defenders where both sides try to best each other constantly. As bots have become more sophisticated, traditional CAPTCHAs are not as effective as they once were.
Over the past two decades, CAPTCHA has evolved in several generations to defend against the increasingly sophisticated bad bots as well as meet the user's needs for a smoother experience. Some of them have been obsolete.
This Captcha takes simple logic: Humans are better than machines at recognizing twisted and warped text letters. It includes:
The superiority of humans over machine programs in recognizing twisted and warped text letters. By introducing noise in the form of different widths, heights, background patterns, borders, and so on, text letters would become easy for humans but hard for machines.
However, with advanced OCR(Optical Character Recognition) technology, computers can recognize distorted and warped texts better than humans. Text-based captcha is easier for bots and harder for humans. Standard CAPTCHA (also known as text-based CAPTCHA) became obsolete in 2014 when google pitted one of its machine learning algorithms against humans on recognizing heavily distorted text.
This Captcha had left the text-based input approach for more innovative challenges that deemed them very difficult for machines to bypass. These challenges included logic puzzles, visual comparisons, movement-based CAPTCHAs, or math challenges.
Even though the second generation of CAPTCHAs looked very different from the first one, the logic behind the challenges stayed very similar: the superiority of humans over machine programs in recognizing images, numbers, or various objects. It includes:
As computer technology advanced and bad bots were becoming better at solving such puzzles, the CAPTCHAs had to be increasingly difficult. As the user friction created by difficult CAPTCHAs has become too severe and the advanced AI technology deemed the second generation gamified CAPTCHAs merely ineffective.
Traditional CAPTCHAs (like reCAPTCHA) have been increasingly outperformed by sophisticated bots developed by spammers and cybercriminals, and they can negatively impact user experience by being cumbersome or difficult to solve, which include:
Today, a better solution is the third generation no-knowledge CAPTCHAs. Advanced CAPTCHA solutions are much easier to pass and infinitely more secure than traditional CAPTCHA solutions.
This advanced CAPTCHA has taken the human verification process into a new dimension by introducing advanced risk analysis into the equation. With no requirement of human thinking, no-knowledge CAPTCHAs have minimum to no interruption to user operations and provide a much better user experience.
It takes the logic: Machines can complete any puzzle challenge, but they can’t imitate genuine human behavior while doing so. This type of challenge presented is mostly irrelevant, and it is only a means to collect data about the user. Security depends on the back-end sophistication of the risk analysis engine.
The traditional CAPTCHAs have lost their effectiveness in advancing technology and cannot satisfy the needs of enterprises. To address this security dilemma, GeeTest created a new generation of CAPTCHA to tell humans and bots apart based on human behavior.
When people are surfing the internet, they will automatically generate biometric information (e.g. mouse track) and environment information (for example, device attributes, browser version). When bots are trying to crack the CAPTCHA, they might use browser automation tools or hack the API, and thus results in environmental discrepancy with legitimate human visitors. Besides the behavior pattern, mouse clicks frequency and other biometric information of bots are also significantly different from humans.
To provide a better user experience, GeeTest CAPTCHA first asks human visitors to click on the captcha button. The biometric data generated through this simple action could be analyzed together with the device attributes to figure out the risk level. If a risk is detected, then the visitors will only be asked to finish a different captcha challenge based on the risk level. By doing this, GeeTest could lower the potential obstacles for visitors as much as possible. GeeTest conducted a test with 30 people and found that it took on average 2.74 seconds to view and solve a GeeTest CAPTCHA, which is far less than the time needed to pass the traditional CAPTCHAs.
Besides, GeeTest CAPTCHA tests are designed for fun! It incorporates the concept of CAPTCHA games and involves up to 9 types of CAPTCHA challenges, which can be customized for a smoother user experience.
If you would like to know more about the advanced CAPTCHA and learn about how it works as an alternative to traditional CAPTCHA in security, convenience, intelligence, and more. Try GeeTest Adaptive CAPTCHA, or register for a free 30-day trial here!
GeeTest
GeeTest
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