Abstract: A blockchain-based video authentication system ensures the integrity, authenticity, and security of digital video content by integrating cryptographic hashing, dynamic watermarking, AI-driven anomaly detection, and decentralized ledger storage. A video file is processed to generate a cryptographic hash, which is immutably stored in a blockchain ledger. A rotating digital watermark is embedded in the video to detect tampering. AI models, including convolutional and recurrent neural networks, analyze frames and motion patterns for manipulation. A fraud detection module leverages generative adversarial networks to identify deepfake alterations. A predictive threat analysis engine employs machine learning to detect emerging manipulation techniques. Verification is enabled via an API, a zero-knowledge proof engine, a browser plugin, and a mobile application. An active watermarking module monitors playback, detecting unauthorized use. A consensus-based validation mechanism ensures tamper resistance.

Abstract: A QR Code Verification Engine provides a multi-layered security framework for generating, validating, and authenticating QR codes while preventing tampering, fraud, and unauthorized access. The system embeds a hidden security layer within the QR code using steganographic encoding or invisible watermarking techniques, ensuring detection of any modifications. The hidden layer is encrypted using asymmetric cryptography, allowing only an authorized verification system to extract and validate it. An AI-powered tamper detection module analyzes QR codes for anomalies, while cryptographic hash verification ensures integrity. The system employs biometric authentication, push notification approvals, and contextual security measures to enhance user verification. Dynamic QR codes with expiration rules prevent replay attacks. Secure offline verification allows authentication without network connectivity.