### Project Motivation and Research Objectives  
It has been made clear that machine learning solutions for audio tampering detection require a dataset for the training of models. Since datasets with real cases of tampered files are not available, most works require the formulation of artificial datasets for model evaluation. Such datasets are often difficult to handcraft, so they follow automated procedures for dataset creation, simulating real-world scenarios. As a result, the implemented models are case- and dataset-specific. There is no evidence for the generalization of the models in multiple scenarios and tampering techniques. For this reason, it is not yet feasible to integrate automated audio authentication into professional workflows without supervision, as they cannot be considered reliable for production and real-world applications. Furthermore, models that are pre-trained in known datasets and conditions may be more vulnerable to adversarial attacks [46].  
On the other hand, traditional audio forensics techniques require expertise and fluency with audio analysis tools. In such an approach, human intelligence and experience play a crucial role in the process of authentication. While this is the most reliable solution and the preferable option in courtrooms, it cannot provide a viable alternative with massive appeal. There is an urgent need for tools that can help in the fight against disinformation. Such tools should be accessible to a broad audience of journalists, content creators, and simple users, to improve the overall quality of news reporting. Average users do not have the expertise to apply audio analysis techniques in the same way as professionals of audio forensics.  
The motivation for the current research emerges from the hypothesis that it is feasible to strengthen a user's ability to recognize tampered multimedia content using a toolbox of supervisory tools provided online through an easy-to-use interface. State-of-the-art approaches for audio analysis and tampering detection were integrated into a web application. The application is available publicly through a web browser. The results of the algorithms do not provide an automated decision-making scheme, but rather a set of visualizations that can assist the user in a semi-automated approach. This means that the framework does not include a model that performs binary classification of files as tampered, or not-tampered, but the final decision is the responsibility of the user, taking advantage of their perception and experience as well as the context of the media asset. Through the use of the application, crowdsourcing is promoted for the creation of a dataset with real-world tampered files for future use.  
The remaining of the paper is structured as follows. In Section 2, the proposed web framework is presented, in terms of the functionality, aims, and technical specifications. The integrated algorithms and their operating principles are listed without emphasizing technical details. In Section 3, the evaluation results from the reverberation estimation models and the initial implementation of the prototype web application are presented. In Section 4, the research results are summarized and discussed, and the future research goals of the project are defined. In Section 5, some of the limitations of the presented research are analyzed.