## 3 Related Works
[29] stands as one of the pioneering works leveraging Machine Learning (ML) for infringement content detection. The proposed methodology hinges on metadata and rule engineering to pinpoint re-uploaded videos on unofficial YouTube accounts. By modifying the Jaro-Winkler distance, this study elucidates the feasibility of measuring the similarity between a query and video metadata, facilitating its classification as either original or infringing. In contrast, Ref. [30] unveils a technique targeting the unauthorized sale, distribution, and display of various software types. The proposed Ant-Miner trend classification, grounded on the Ant Colony Optimization (ACO) algorithm, is proficient in categorizing the nature of the infringing software. Expanding on ML applications, [PERSON]. [31] harnesses the prowess of BERT for text summarization. The paper delineates a framework for classifying YouTube videos by modeling topics embedded in brief video descriptions. Such an approach refines DMCA reporting accuracy. Simultaneously, [PERSON]. [32] emphasizes DL potential in detecting infringements. By deploying DL architectures like AlexNet, ResNet, and a tailored eight-layer DNN, the study successfully identifies re-transmitted banners and logos from streaming services across various platforms. Taking a web-centric perspective, [PERSON]. [33] champions a web crawling approach coupled with web scraping sensors. The intent is to capture iconography indicative of unauthorized content display or download. Through a marriage of Support Vector Machine (SVM) and Word Embedding techniques, the methodology excels in identifying sites with potentially unauthorized download tendencies based on headers or logos. In a related vein, Ref. [34] proposes web monitoring through clusters of web scraping sensors. By extracting text, image, and metadata features, a Multi-Tasking Ensemble Algorithm (MTEA) is trained to ascertain unauthorized video streams. Lastly, Ref. [35] further delves into the capabilities of MTEA for pinpointing unauthorized video streams across diverse platforms. The approach considers chat message ratios and sentiment polarities to gauge a video's popularity vis a vis the original. Complemented by metadata extraction and object recognition, the initial video banner is then compared against the original catalog, earmarking the infringed content. In order to understand and summarize the capabilities and perspectives of the studies presented in this section, a series of criteria were used, as detailed below:  
* Data Collection Mechanism: this aspect evaluates whether the study implements a mechanism for gathering data to acquire samples over the IoE. * Use of Known Categories: this study is based on a list, white paper, or report that has assessed the approach as being targeted toward a prevalent issue of infringing multimedia content. * Employment of NLP Methods: it relies in some manner on textual comparison, whether through titles, content, or optical character recognition, to achieve its objective. * AI/ML Algorithm Utilization: One or more AI/ML algorithms are employed for predictive, regression, or classification tasks. * Performance Reporting: at least some measure of performance is documented. Therefore, in accordance with the criteria previously outlined, Table 3 sets out the comparison with the methodology (BERT + DNN) presented herein.