Bibliometric analysis reveals growth in video steganography and compression-aware designs for H.264, suggesting significant advancements in data hiding methods.
The need for secure and robust multimedia communication has driven significant advances in video steganography. This paper proposes an improved frame selection-based data hiding scheme that combines analysis of object motion, modified entropy, and the psychovisual threshold of Discrete Cosine Transformation (DCT) to achieve high visibility and robustness against H.264 compression. A published bibliometric study from 2021–2025 reveals strong research growth in motion vector domain embedding, hybrid DCT deep learning approaches, compression-aware design for H.264/HEVC, and integration of encryption with error correction for better endurance. The results of the experiment using 14 benchmark videos showed that the proposed scheme achieved higher Signal to Peak Noise Ratio (PSNR) and Cross Correlation (NC) values compared to the latest method, in line with current research trends.
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Abdullah et al. (2025) studied this question.
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