<![CDATA[In an era of escalating cybersecurity threats, traditional authentication methods become more susceptible to attacks like phishing, brute force, and identity theft. With the aim to counter these difficulties, this paper introduces a multi-layered authentication that merges facial recognition, eye-tracking based Morse code verification, biometric verification using convolutional neural network (CNN) and cryptographic watermark with Rubik's encryption. The document fortification system proposed here improves security by integrating biometric authentication, behavioral verification, and encryption-based watermarking to provide both user authentication and document integrity. The authentication process begins with facial recognition, where multi-task cascaded convolutional neural network (MTCNN) detects facial features and FaceNet generates unique embeddings for identity verification. Upon successful authentication, users input a Morse code password via an eye-blinking mechanism, which is decoded and validated. Additionally, fingerprint and iris recognition using CNN models further enhance security. The Rubik’s encryption algorithm secures biometric watermarks within digital documents, preventing tampering. An one-time password (OTP)-based re-authentication mechanism ensures only authorized users can access encrypted files. Experimental results demonstrate the system’s high accuracy and resilience against security threats, making it a robust and scalable authentication framework. This research highlights the potential of multi-factor authentication (MFA) in modern cyber-security, offering a future-ready solution for securing sensitive digital documents such as images and pdf files.]]>
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