## 2 Taxonomy for the Protection and Detection of Infringing Multimedia Content  
In the context of research on copyright infringements within the IoE, multiple studies were discovered, marked by a wide variety of methods, techniques, procedures, and approaches that still lack standardization. This represents a considerable challenge in defining a starting point for projects that are to be implemented. The developed taxonomy offers significant advantages, such as the ability to identify how the dynamics of copyright protection or infringement manifest, the structuring of the techniques used, maintaining coherence between legal and technical tasks, integrating new paradigms in AI/ML, as well as its role as a tool adaptable to emerging information technologies. To synthesize the findings, the taxonomic architecture is centered around two primary pillars: the studies that focus on safeguarding digital multimedia materials and those that actively operate by detecting where infringing activity is taking place. The study was directed using a highly effective process for conducting searches of documents about the state-of-the-art, known as Systematic Reviews and Meta-Analyses (PRISMA) [37]. This method allows for the establishment of a set of explicit criteria to find, filter, and integrate studies, avoiding redundancy or getting sidetracked by searches that lead to irrelevant outcomes. In Figure 2, the flow of filters used to consolidate the number of relevant records in this research is presented. To compile an appropriate collection of relevant records, several major scientific publication houses and digital libraries were inspected: EBSCO's Academic Search [38], Taylor and Francis Online [39], Springer Link [40], Elsevier Science Direct [41], Oxford Academic [42], Wiley Online Library [43], Scopus [44], IEEE Explore [45], ACM Digital Library [46], and MDPI [47]. This task was undertaken using search terms such as _digital piracy detection_, _Machine Learning content protection_, _digital rights management with AI_, _copyright infringement_, _multinedia content tracking_, _intellectual property protection_, and _digital content protection_, to name a few. In summary, a compilation of 44 studies was achieved, divided into 23 within the safeguarding domain and 21 in the active domain, which are detailed in the following paragraphs:  
In the realm of safeguarding digital assets, twenty-three significant research works [6, 7, 19, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] have been identified. Of these, nine [6, 7, 19, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 50, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60] focus on the use of legal resources and partially address technical aspects. Ten of these works [25, 53, 54, 55, 56, 57, 58, 59, 60] adopt a fully technical stance on defensive strategies. However, only four studies [25, 53, 54, 55, 56, 57, 58, 59, 60] are dedicated to protection models that integrate AI and ML-based technologies. On the other hand, in the active identification of infringing sites, twenty-one [25, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 50, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63] significant research contributions were compiled. Among these, three [65, 66, 67] engage in non-technical actions such as manual search and reporting, contributing to the legal takedown of offending sites. Six projects [28, 29, 68, 69, 70, 71] follow a technical route, involving surveillance and cyber patrolling within the IoE. Regrettably, only twelve initiatives [28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 52, 49, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 72, 73, 74, 75] implement one or more AI/ML techniques for identifying infringing content on the IoE, and of these, only eight [28, 29, 30, 31, 32, 33, 34, 35] are considered closely related studies. Notably, a single article [25] stands out for its purely technical approach to the defensive and active aspects.