An Efficient Video Steganography Approach for Secure and Low-distortion Data Embedding and Extraction

The growing requirement for secure multimedia communication has tested typical video steganography methods, which often trade off embedding capacity, visual quality, and signal-processing robustness. This study proposes an adaptive Discrete Wavelet Transform (DWT)-based video steganography architecture that uses key-based pseudo-random frame selection and adaptive coefficient embedding to hide data safely and undetected. Two layers of Haar DWT deconstruct selected video frames, and a dynamic scaling factor based on local and global statistical parameters encodes hidden information in LH and HL sub-bands. We test the proposed system on the UCF101 dataset of human action videos under Gaussian noise, JPEG compression, filtering, and frame dropping. Experimental results demonstrate that the recommended approach generates a high peak signal to noise ratio (PSNR) of 43.85 dB and a low MSE of 4.12, resulting in excellent visual clarity with reduced distortion. The suggested framework has a maximum embedding capacity of 0.58 bpp, outperforming LSB, DCT, and DWT. Robustness analysis shows a bit error rate < 0.02 with common attacks, ensuring data recovery and signal resilience. Results reveal that the adaptive DWT design balances security, embedding efficiency, and visual quality, making it suitable for secure multimedia transmission and digital content protection.