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The Rapid Growth Of Multimedia Communication Has Intensified Interest In Video Steganography As A Means Of Secure Data Concealment And Transmission. Unlike Traditional Steganographic Methods That Focus Primarily On Imperceptibility And Payload Capacity, Modern Threats Demand Stronger Resilience, Adaptability, And Layered Security. This Work Explores Advancements In Video Steganography Enhanced By Multifactor Authentication (MFA) Mechanisms And Powered By Convolutional Neural Networks (CNNs). CNNs Are Employed To Learn Robust Feature Representations For Both Embedding And Extraction, Improving Imperceptibility While Resisting Steganalysis Attacks. By Integrating MFA—including Biometric, Cryptographic, And Behavioral Verification—the Framework Ensures That Concealed Data Remains Inaccessible To Unauthorized Parties Even If Extraction Is Attempted. Experimental Evaluation Demonstrates Improved Payload Fidelity, Higher Peak Signal-to-noise Ratio (PSNR), And Reduced Detection Rates Against Conventional Steganalysis, While Authentication Layers Significantly Strengthen Data Confidentiality. The Proposed Approach Highlights A Secure, Intelligent, And Resilient Paradigm For Video Steganography, Suitable For Applications In Defense, Healthcare, And Digital Forensics Where Both Covert Communication And Stringent Access Control Are Essential.