This review evaluates developments in video steganography, identifying pitfalls and suggesting future improvements.
Video is an ideal medium for covert communication due to its traffic volume and the many possible embedding domains offered by motion-compensated compression. This review of 30 years of video steganography research, including steganalysis and deep learning approaches, proposes a unified taxonomy and uses it to map developments across 347 sources. Many of these sources simply apply image steganography to video, and only a few critically evaluate security. The review identifies connections between these papers and highlights common pitfalls that hinder comparability. The state of the art is characterized by a discussion of selected landmark papers in each embedding domain, followed by recommendations for future work.
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Lachner et al. (2026) studied this question.
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