## 3 Related Works  
As outlined in Section 2, eight studies [28; 29; 30; 31; 32; 33; 34; 35] were identified as closely related works, meeting specific criteria: they employ traversal techniques across the IoE or other related \begin{table}
\begin{tabular}{c c c} \hline \hline  & & Rights Enforcement Organizations (REOs) address complaints of potential copyright infringements over the IoE. Upon identification by the author or representative, this can lead to legal actions, ranging from takedown notices to lawsuits, compensation, and further prosecution. \\ \cline{2-3}  & & Legal tools urging (Internet Service Providers) ISPs and Online Service Providers (OSPs) to adhere to applicable laws within a clear legal framework. Actions can lead to contract terminations with infringers, fines, and penalties for not addressing copyright holder warnings. \\ \hline  & & Techniques that navigate through catalogs, explicit requests, databases, or real-time updates of multimedia works using various IoE search engines. These can quantify the similarity of the results yielded from queries with attributes of the retrieved web documents, such as the title, metadata, information embedded in HyperText Markup Language, (HTML), eXtensible Markup Language (XML), JavaScript Object Notation (JSON) dictionaries, hyperlinks, iframes, and URLs. \\ \cline{2-3}  & & Using web crawling techniques on the IoE, specific objectives are monitored using robot-type software or web scrapers to track changes on selected sites. Infringement patterns in multimedia files are recorded, and Data Mining procedures evaluate the content to determine the type of infringement. \\ \cline{2-3}  & & Advanced query configurations on search engines within the IoE aim to match and flag specific multimedia files based on their title, size, metadata, extensions, or other relevant details. These can enhance the identification of infringing websites where the multimedia material is being disseminated. Noteworthy platforms include Google Image Reverse Search [76], TinEye [77], and Yandex [78]. \\ \hline  & & It is a novel and experimental technique that employs cyber patrolling using AI, ML, DL, and NLP algorithms to traverse numerous search engines and meta-search engines within the IoE to summarize, classify, and group websites displaying signs of infringement due to possession of multimedia files. It has also expanded to Online Social Networks (OSNs) and the DarkNet [30; 73]. \\ \cline{2-3}  & & It is a prototype technique that applies Optical Object and Character Recognition (OCR) to determine whether unauthorized advertisements, promotions, offerings, and sales of a multimedia file can be found within the visual content of the website in question. This involves exploring the DOM and converting each image to text for subsequent evaluation. \\ \cline{2-3}  & & Converts text found within IoE resources into detailed contextual representations, which are designed to detect similarities in potentially infringing content. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Domain, subdomains, and categories related to identifying and taking control over multimedia files in the IoE that infringe on copyright. networks, their methodologies incorporate one or more AI/ML algorithms for identifying websites with potentially infringing multimedia content, and they pertain to key consumption categories as defined in the referenced literature. The following paragraphs provide a detailed overview of each of these publications. The authors in [28] introduced a methodology to monitor local network traffic, aiming to identify users leveraging P2P protocols for unauthorized content downloads. Utilizing an audio-video fingerprinting system named The CopySense Appliance, that study demonstrated that by inspecting frames within, P2P, TCP, and UDP protocols, traces of non-original multimedia files can be detected, cross-referencing the DMCA database. The findings suggest an imperative for more advanced mechanisms to bolster similarity-based detections. Moreover, Ref.