Video steganography has become an essential technique for secure communication, watermarking, and copyright protection in the face of increasing cyber threats and surveillance. Unlike encryption, which often draws attention, steganography embeds sensitive information within multimedia content. Videos, with their large size and redundancy, offer unique opportunities for covert data transmission. While several surveys exist, most either focus broadly on multimodal steganography or provide only limited coverage of video-based methods. They often lack a systematic comparison across spatial, transform, and compressed domains, insufficiently analyse trade-offs among imperceptibility, robustness, and embedding capacity, and rarely address practical constraints such as computational cost, dataset limitations, and resistance to modern steganalysis. This review fills these gaps by offering a taxonomy driven analysis of video steganography techniques. Methods such as Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), motion vector modification, and hybrid approaches are critically compared using standardized performance metrics. Beyond cataloguing techniques, we evaluate vulnerabilities, highlight limitations in existing research, and propose a roadmap for future developments. Future perspectives emphasize deep learning based adaptive embedding, blockchain-enabled authentication, and optimization strategies for enhanced efficiency and scalability. Thus, this paper contributes a critical, domain specific roadmap that advances secure video steganography beyond the scope of earlier surveys.
Suresh et al. (Fri,) studied this question.