A Web Authentication Biometric 3D Animated CAPTCHA System Using Artificial Intelligence and Machine Learning Approach

A Web Authentication Biometric 3D Animated CAPTCHA System Using Artificial Intelligence and Machine Learning Approach

Authors

DOI:

https://doi.org/10.37965/jait.2023.0216

Keywords:

artificial intelligence, B3DA, CAPTCHA, DDOS, image recognition, OCR

Abstract

The Internet and web security are integral aspects of our daily lives. Many commercial firms provide clients with Internet services. For web access, it is assumed that only the genuine user, who is a human, will register. Yet automated hacking programs can also do registrations with fake data that consume a lot of bandwidth, slowing down or occasionally even shutting down websites, leading to Distributed denial-of-service attacks. Completely Automated Public Turing test to tell Computers and Human Apart (CAPTCHA) is the solution. Complex CAPTCHA is challenging for humans to recognize, but simple CAPTCHA is simple for AI to decipher. With the developments in neural networks and machine learning, bots are mimicking humans, and it is becoming difficult to distinguish humans and bots apart. This generated a need to think of some more innovative and novel CAPTCHA. Now, utilizing the same AIML approach to increase the efficacy of CAPTCHA and make it stronger against the bot attack. Biometric 3D Animated Algorithm proposed in this research is a novel approach based on the Face Detection AI algorithm along with handwritten 3D animated characters selected randomly to create a string which makes CAPTCHA simple that humans can identify but very difficult for bots. The test results have proven this to be a very robust CAPTCHA. The machine learning algorithm employed will keep on increasing th efficacy of this CAPTCHA each time the bot tries to break this.

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Published

2023-05-12

How to Cite

Neha Pradyumna Bora, & Dinesh Chandra Jain. (2023). A Web Authentication Biometric 3D Animated CAPTCHA System Using Artificial Intelligence and Machine Learning Approach. Journal of Artificial Intelligence and Technology, 3(3), 126–133. https://doi.org/10.37965/jait.2023.0216

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Section

Research Articles
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