Persona:
Pastor Vargas, Rafael

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0000-0002-4089-9538
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Pastor Vargas
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Rafael
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  • Publicación
    Dataset Generation and Study of Deepfake Techniques
    (Springer, 2023) Falcón López, Sergio Adrián; Robles Gómez, Antonio; Tobarra Abad, María de los Llanos; Pastor Vargas, Rafael
    The consumption of multimedia content on the Internet has nowadays been expanded exponentially. These trends have contributed to fake news can become a very high influence in the current society. The latest techniques to influence the spread of digital false information are based on methods of generating images and videos, known as Deepfakes. This way, our research work analyzes the most widely used Deepfake content generation methods, as well as explore different conventional and advanced tools for Deepfake detection. A specific dataset has also been built that includes both fake and real multimedia contents. This dataset will allow us to verify whether the used image and video forgery detection techniques can detect manipulated multimedia content.
  • Publicación
    Forensic Technologies to Automate the Acquisition of Digital Evidences
    (IEEE, 2022) García Guerrero, David; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael
    The main goal of this work is to propose the automatic acquisition of evidences in a remote way. This automated capacity becomes interesting for companies with extensive networks and/or several locations, as it allows them to delegate and centralize the acquisition task at a single point in their structure, while saving time and travel costs. This research has been carried out through the initial implementation of a virtual laboratory made up of a network and different scenarios, by including an experimentation process. The virtual network includes both the machine from which automatic acquisitions are performed and the devices from retrieving the evidence. The group of devices will be made up of various experiments. The aim is to analyze the viability of the acquisition in different scenarios, since distributed networks are not homogeneous in the real world