Persona:
Pastor Vargas, Rafael

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ORCID
0000-0002-4089-9538
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Proyectos de investigaciĆ³n
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Pastor Vargas
Nombre de pila
Rafael
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2016 - 201912020 - 20232
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Tiene archivos: true Ɨ Tipo: conference proceedings Ɨ

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Mostrando 1 - 4 de 4
  • Miniatura
    PublicaciĆ³n
    Dataset Generation and Study of Deepfake Techniques
    (Springer, 2023) FalcĆ³n LĆ³pez, Sergio AdriĆ”n; Robles GĆ³mez, Antonio; Tobarra Abad, MarĆ­a 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.
  • Miniatura
    PublicaciĆ³n
    Forensic Technologies to Automate the Acquisition of Digital Evidences
    (IEEE, 2022) GarcĆ­a Guerrero, David; Tobarra Abad, MarĆ­a de los Llanos; Robles GĆ³mez, 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
  • Miniatura
    PublicaciĆ³n
    Researchersā€™ perceptions of DH trends and topics in the English and Spanish-speaking community. DayofDH data as a case study
    (Jagiellonian University & Pedagogical University (Cracovia), 2016-07-22) GonzĆ”lez-Blanco GarcĆ­a, Elena; Rio Riande, Gimena del; Robles GĆ³mez, Antonio; Ros MuƱoz, Salvador; HernĆ”ndez Berlinches, Roberto; Tobarra Abad, MarĆ­a de los Llanos; Caminero HerrĆ”ez, AgustĆ­n Carlos; Pastor Vargas, Rafael
  • Miniatura
    PublicaciĆ³n
    Detection of Cerebral Ischaemia using Transfer Learning Techniques
    (IEEE) AntĆ³n MunĆ”rriz, Cristina; Haut, Juan M.; Paoletti, Mercedes E.; BenĆ­tez Andrades, JosĆ© Alberto; Pastor Vargas, Rafael; Robles GĆ³mez, Antonio
    Cerebrovascular accident (CVA) or stroke is one of the main causes of mortality and morbidity today, causing permanent disabilities. Its early detection helps reduce its effects and its mortality: time is brain. Currently, non-contrast computed tomography (NCCT) continues to be the first-line diagnostic method in stroke emergencies because it is a fast, available, and cost-effective technique that makes it possible to rule out haemorrhage and focus attention on the ischemic origin, that is, due to obstruction to arterial flow. NCCT are quantified using a scoring system called ASPECTS (Alberta Stroke Program Early Computed Tomography Score) according to the affected brain structures. This paper aims to detect in an initial phase those CTs of patients with stroke symptoms that present early alterations in CT density using a binary classifier of CTs without and with stroke, to alert the doctor of their existence. For this, several well-known neural network architectures are implemented in the ImageNet challenges (VGG, NasNet, ResNet and DenseNet), with 3D images, covering the entire brain volume. The training results of these networks are exposed, in which different parameters are tested to obtain maximum performance, which is achieved with a DenseNet3D network that achieves an accuracy of 98% in the training set and 95% in the test set