“Web Security Through Gesture Based CAPTCHA Using CNN Deep Learning Algorithm”

In the contemporary era, advanced technology is pervasive across various domains, underscoring the importance of security measures. CAPTCHAs were devised to ensure security by verifying whether the user is human or not. Serving as a reverse Turing test, CAPTCHA distinguishes between human users and automated bots. Widely employed in the internet industry for cyber security, an effective CAPTCHA should be simple for humans to solve but challenging for machines. However, numerous existing CAPTCHAs, reliant on visual recognition, are becoming increasingly susceptible to exploitation due to advancements in visual recognition technologies. CAPTCHA can solely be deciphered by humans, thereby thwarting hackers and their automated crawlers (spiders) from infiltrating restricted sections of the internet. Nonetheless, conventional CAPTCHAs have become time-intensive and necessitate substantial human effort to impede automated programs from circumventing them, resulting in wasted time. This paper proposes an enhanced CAPTCHA model utilizing hand gesture recognition techniques, offering superior efficiency compared to traditional CAPTCHAs.