COMPUTATIONAL SYSTEM FOR EVALUATING HUMAN PERCEPTION IN VIDEO STEGANOGRAPHY

This paper presents a comprehensive computational system designed to evaluate the undetectability of video steganography from human perspective. The system assesses the perceptibility of steganographic modifications to the human eye while simultaneously determining the minimum encoding level required for successful automated decoding of hidden messages. The proposed architecture comprises four subsystems: steganogram database preparation, human evaluation, automated decoding, and comparative analysis. The system was tested using example steganographic techniques applied to a dataset of video files. Experimental results revealed the thresholds of human-level undetectability and automated decoding for each technique, enabling the identification of critical differences between human and algorithmic detection capabilities. This research contributes to the field of steganography by offering a novel framework for evaluating the trade-offs between human perception and automated decoding in video-based information hiding. The system serves as a tool for advancing the development of more secure and reliable video steganographic techniques.