Completely Automated Public Turing Tests to Tell Computer and Humans Apart (CAPTCHA) is an approach used to distinguish between a self-active computer program and a human. This kind of CAPTCHA is used in the field of computing to discover a particular user interacting with the system is human or not. It requires someone to enter the correct sequence of digits, characters, or both the combinations. The average time taken to solve the current captcha displayed will take around 10 s approximately. Research findings also discover additionally that the numerous famous CAPTCHA procedures are not powerful or secure, further entangling the test of offering services acquired from robotic intervention yet available to individuals with disabilities. This leads to the formulation of new approaches like QR Code Scanning-to-Speech, Revamping, and Randomness. This technique is encrypted end-to-end by using Blowfish Fermat Little Theorem (BFLT). There are a total of 156 physical users (both visually impaired and visually fit) were analyzed to assess the potency of the proposed approach. Further, it is compared with the current highest development CAPTCHA to test the performance of the QR Code Scanning-to-Speech, Revamping, and Randomness. The security scrutiny proved that the proposed approach is robust and hindering Hidden Markov Model (HMM), Fuzzy solver, and recent eminent attacks.

完全自动化的公用图灵测试以区分计算机和人类（CAPTCHA）是一种用于区分自动计算机程序和人类的方法。这种CAPTCHA用于计算领域，以发现与系统交互的特定用户是人类还是非人类。它要求有人输入正确的数字，字符序列或两者的组合。解决当前显示的验证码所需的平均时间大约为10 s。研究发现还另外发现，许多著名的CAPTCHA程序功能不强大或不安全，这进一步纠结了从机器人干预中获得但仍可供残障人士使用的提供服务的测试。这导致了新方法的制定，例如QR码语音转换，改编和随机性。通过使用河豚费马小定理（BFLT）端对端加密此技术。总共对156个物理用户（包括视力障碍者和视觉适应者）进行了分析，以评估该方法的有效性。此外，将其与当前发展最快的CAPTCHA进行比较，以测试QR码语音扫描，改编和随机性的性能。安全审查证明，该方法是健壮的，并阻碍了隐马尔可夫模型（HMM），模糊求解器和近期的重大攻击。将其与当前发展最快的CAPTCHA进行比较，以测试QR码语音扫描，翻新和随机性的性能。安全审查证明，该方法是健壮的，并阻碍了隐马尔可夫模型（HMM），模糊求解器和近期的重大攻击。将其与当前发展最快的CAPTCHA进行比较，以测试QR码语音扫描，翻新和随机性的性能。安全审查证明，该方法是健壮的，并阻碍了隐马尔可夫模型（HMM），模糊求解器和近期的重大攻击。