## 4 Discussion  
An AudioVisual Authentication application is presented, part of the MAthE project on computer-aided support of journalists and simple users against misinformation. It specializes in the authentication of audiovisual content in an audio-driven technical approach. It has the form of a web application and implements the functionality of a framework that promotes machine-assisted, human-centered decision making, collective intelligence, and collaboration in the battle against the malicious tampering of audiovisual content. The functionality of the application is provided to the end-users through a very simple and intuitive interface where the user is asked to provide the FUI. The toolbox features several signal processing modules that are applied to the FUI, providing an interactive graph that contains several visualizations. These are based on different technical principles and algorithms that aim to assist the user who makes the final decision. Through crowd  
Figure 4: In Contribute mode, users can browse files from the database along with their analysis visualizations, and annotate them concerning the detection of audio tampering.  
sourcing, a dataset of real-world tampered files is expected to be created and validated for the first time.  
Along with a set of algorithms that are based on previous research, a CNN regression model for SRR estimation was presented and evaluated. The model performs SRR estimation with an MSE of 0.029, which is an acceptable resolution for the detection of different acoustic environments. Of course, like all the proposed techniques, it has several limitations, especially regarding studio recordings, using files with similar room acoustics for tampering, or simulating the reverberation environment to match the excerpts used for copy-move forgery.  
The main contributions of the research are summarized as follows:  
1. A novel approach is proposed for audio tampering detection, where decision making is held by the human-in-the-loop in a computer assisted environment. This approach makes use of technical advances, surpasses their limitations and unreliability, and proposes a solution that can be immediately applied in journalistic practice.
2. The solution is provided openly as a service, allowing its use by journalists and the audience, without any limitations on their equipment or platform.
3. The application follows a modular approach. This means that the modules that are integrated in the prototype can be updated easily, and more modules can be added in the near future.
4. A CNN model for data-driven SRR estimation to be used in the direction of audio authentication was presented and evaluated.
5. A crowdsourcing approach was introduced for both user collaboration in media authentication and dataset creation and annotation. Users contribute with their effort to the extension of the dataset of tampered media files and also assist other users who request support in the authentication of specific files.  
Since this is the initial launch of the application, future research goals include a thorough evaluation of the interface and the provided tools. This can be done in focus groups containing professionals and also publicly, to evaluate how a broader audience receives the application. Such workshops can also provide publicity for the project. Crowdsourcing is a core aspect of MAthE, so it is crucial to approach potential users and engage them in using the application to collect initial results. The major outcome is expected to be the dataset, which will be publicly available. After the formulation of the initial dataset, more intense experimentation on the applicability of different machine learning architectures can take place. Moreover, the involvement of more contributors from the engineering world (researchers, students, coders, etc.) can aid the improvement and extension of the provided toolbox. For this reason, all the necessary material will be also publicly accessible through the application's website.