In another survey paper, [PERSON] [4] presents a systematic literature review of the most recent approaches for counteracting network covert channels. Network covert channels are increasingly used to distribute malware (stegomalware) and the detection of these kinds of attacks is difficult since those channels typically carry very low data rates, and it is difficult to identify where the secret information is hidden. Apart from the literature review, the paper identifies the major research challenges and the most promising development trends and discusses the advancements needed for building a more robust and consistent framework to obtain protection against network covert channels and stegomalware. Among the research papers, four of them focus on new watermarking applications, including watermarking for 3D models [5], a watermarking scheme robust against screencam attacks [6], a watermarking protocol based on blockchain [7], and a watermarking framework to protect Deep Learning (DL) models [8]. In Ref. [5], [PERSON] et al. present an algorithm for integrity protection of 3D models represented as a set of vertices and polygons. The proposed approach uses fragile watermarking on the vertices of the 3D model introducing a very small watermarking noise. The watermark is embedded into a secret vector space defined by the Karhunen-Loeve transform obtained from a key image. The proposed method outperforms the state-of-the-art techniques in terms of performance, especially distortion and security. [PERSON] et al. [6] present a screen-cam robust image watermarking scheme using feature-based synchronization. Taking pictures of a screen with mobile phones or cameras is one of the main methods to leak image information. The paper analyzes the distortions caused by the screen-cam process and classifies them into five categories, namely, linear distortion, gamma tweaking, geometric distortion, noise attack, and low-pass filtering attack. After that, a watermark synchronization method is proposed based on the construction of a local square feature region (LSFR), a Gaussian function, a modified Harris-Laplace detector, and a speeded-up robust feature (SURF) orientation descriptor. Then, the message is embedded in each selected LSFR with an embedding algorithm using a non-rotating embedding method and a preprocessing procedure to modulate the discrete Fourier transform (DFT) coefficients. This approach makes it possible to improve watermark detection from the captured information. The effectiveness of the method against screen-cam attacks is shown and the scheme outperforms the state-of-the-art techniques not only against screen-cam attacks, but also when they are combined with other desynchronization attacks. In Ref. [7], [PERSON] presents a watermarking protocol based on blockchain. \"Buyer friendly\" and \"mediated\" watermarking protocols can ensure both correct content protection and the easy participation of buyers in the transactions to purchase multimedia content, offering an alternative to more traditional \"buyer and seller\" watermarking protocols. In this paper, a new watermarking protocol is proposed combining the \"buyer friendly\" and \"mediated\" approaches with blockchain technology. The resulting protocol supports limited and balanced participation of buyers and content providers in transactions of protected digital content and avoids the need for a trusted third party directly involved in transactions. This reduces the risk of protocol violations by the buyers or the sellers by colluding with other parties. The proposed approach achieves security properties that are comparable to those of the state-of-the-art schemes, which require trusted third parties. Although the performance is somewhat penalized by the consensus mechanism of the blockchain, the next generations of the blockchain will make it possible to implement better algorithms and improve performance. [PERSON] et al. [8] propose KeyNet, an asymmetric key-style framework for watermarking DL models.