The exact time spans depend on the deployed variant of the covert channel, which, in turn, is affected by the width of the selected hash function. Finally, two other application scenarios are presented in the remaining papers of this special issue. First, an adaptive reversible data hiding scheme is proposed [13] and, second, a Machine Learning (ML)-based solution for the detection of propaganda and misinformation spread using mixed-code text is presented [14]. In [13], [PERSON] et al. propose an adaptive reversible data hiding scheme using absolute moment block truncation coding (AMBTC) and Quantization Level Difference (QLD). Reversible data hiding makes it possible to recover the original (cover) object after data extraction in the receiving end, being a very valuable application field when images must be protected even from the slightest modification (such as in medical and military applications). The proposed method provides a large embedding capacity with a reduced quality loss for the modified images and uses QLD together with an interpolation technique to adaptively embed the secret information into pixels of AMBTC-compressed block, except for the positions of two replaced quantization levels. The proposed method obtains good performance for embedding capacity and still meets the requirement for better-modified image quality when the image is complex thanks to the use of QLD. The experimental results show that the proposed method outperforms the referenced approaches regarding distortion in terms of peak signal-to-noise ratio (PSNR) and hiding capacity. Finally, [PERSON] et al. [14] present an algorithm for the detection of hidden propaganda in mixed-code text over the internet. The internet has become a widely used tool for spreading misinformation and propaganda. Although mechanisms to track potentially fake information exist, various ways have been found to avoid such kind of detection. An example of the approaches used to bypass detection is to use mixed-code language, which consists of text written in an unconventional way combining different languages, symbols, scripts, and shapes, often substituting the usual alphabet letters. The message is still readable by humans, but algorithms often fail in their detection. The article proposes a machine learning (ML) approach for character identification to detect and analyze whether some content includes propaganda or disinformation hidden using mixed-code text. The paper first proposes a possible categorization of different types of existing mixed code. The study is focused on the analysis of one type of _Text as Art Form_ written on a single row by adopting a methodological approach centered on two main aspects, namely mixed code text analysis and hidden propaganda detection. Regarding the mixed code text analysis, a four-step algorithm that supports both the identification of mixed code in the text along with its normalization into natural language is proposed. As the hidden propaganda detection is concerned, a Convolutional Neural Network (CNN) classifier is designed. The overall performance of the tests is on a publicly available dataset containing a collection of 15,847 textual propaganda and nonpropaganda-related items. The experimental results are very remarkable in terms of performance (accuracy, precision, F1-score, and recall), overcoming the achievements of the reference works. In general, this special issue covers recent trends in both traditional and emerging applications of data hiding and constitutes a good sample of the current state-of-the-art results in this field. The authors contributed equally to the different steps of this research. All authors have read and agreed to the published version of the manuscript. The authors acknowledge the funding obtained from the EIG CONCERT-Japan call to the project Detection of fake newS on Social MedIa pLAftoRms \"DISSIMILAR\" through grantsPCI2020-120689-2 (Spanish Government), JPMJSC20C3 (Japanese Government) and EIG CONCERT-JAPAN/05/2021 (National Centre for Research and Development, Poland).