Persona: Pastor Vargas, Rafael
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rpastor@scc.uned.es
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0000-0002-4089-9538
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Pastor Vargas
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Rafael
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Publicación Forensic Analysis of Manipulated Images and Videos(MDPI, 2025-11-29) Falcón-López, Sergio A.; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, RafaelThe transition from Industry 4.0 to Industry 5.0 emphasizes the need for ethical, transparent, and human-centric artificial intelligence systems. In this context, ensuring the authenticity of digital information has become crucial for maintaining societal trust. This study addresses the challenge of detecting manipulated multimedia content, including synthetic images, videos, and audio generated by artificial intelligence, commonly known as Deepfakes. We analyze and compare general-purpose and Deepfake-specific detection methods to assess their effectiveness in real-world scenarios. This work introduces a refined reference model that integrates both application-oriented and methodological criteria, grouping tools into Blind Forensic, Handcrafted Machine Learning, Deep Learning-based methods, and Toolkits. This structured taxonomy provides a clearer comparative framework than existing works, which typically classify detectors using only one of these dimensions. To ensure reproducible evaluation, all experiments were performed using the SAFL dataset, which consolidates real and synthetic multimedia content generated with publicly available tools under a unified protocol. Among the tested tools, Forensically achieved the highest accuracy in image forgery detection 86.9%, while Autopsy reached 69.5% among Deepfake-specific image detectors. In video analysis, Forensically obtained 98.6% accuracy, whereas Deepware Scanner achieved 91.2% as the most effective Deepfake-focused tool. These results highlight that general-purpose methods remain robust for images, while specialized detectors perform competitively in videos. Overall, the proposed model and dataset establish a consistent foundation for advancing hybrid detection strategies aligned with the ethical and transparent AI principles envisioned in Industry 5.0.Publicación Forensic Analysis Laboratory for Sport Devices: A Practical Case of Use(MDPI, 2023) Donaire Calleja, Pablo; Robles Gómez, Antonio; Tobarra Abad, María de los Llanos; Pastor Vargas, RafaelAt present, the mobile device sector is experiencing significant growth. In particular, wear- 1 able devices have become a common element in society. This fact implies that users unconsciously 2 accept the constant dynamic collection of private data about their habits and behaviours. Therefore, 3 this work focuses on highlighting and analyzing some of the main issues that forensic analysts face 4 in this sector, such as the lack of standard procedures for analysis and the common use of private 5 protocols for data communication. Thus, it is almost impossible for a digital forensic specialist to 6 fully specialize in the context of wearables, such as smartwatches for sports activities. With the aim 7 of highlighting these problems, a complete forensic analysis laboratory for such sports devices is 8 described in this paper. We selected a smartwatch belonging to the Garmin Forerunner Series, due to 9 its great popularity. Through an analysis, its strengths and weaknesses in terms of data protection 10 are described. We also analyze how companies are increasingly taking personal data privacy into 11 consideration, in order to minimize unwanted information leaks. Finally, a set of initial security 12 recommendations for the use of these kinds of devices are provided to the reader.Publicación Detection of Cerebral Ischaemia using Transfer Learning Techniques(IEEE) Antón Munárriz, Cristina; Haut, Juan M.; Paoletti, Mercedes E.; Benítez Andrades, José Alberto; Pastor Vargas, Rafael; Robles Gómez, AntonioCerebrovascular 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 setPublicación Teaching cloud computing using Web of Things devices(IEEE, 2018) Carrillo, J. Cano; Pastor Vargas, Rafael; Romero Hortelano, Miguel; Tobarra Abad, María de los Llanos; Hernández Berlinches, RobertoThis work deals with the teaching of the innovative technology, named cloud computing, using the Web of Things (WoT) platform model based on web services. These services are designed and programmed by the students to handle embedded hardware devices (things) on Internet. The course is carried out within a makerspace where our students can take advantage of valuable on-line tools which are available in a collaborative learning environment. The introduction of these innovative technological elements improves the students' interest and engagement leading to achieve better learning results.Publicación Cerebral ischemia detection using Deep Learning techniques(Springer, 2025-05-20) Pastor Vargas, Rafael; Antón‑Munárriz, Cristina; Haut, Juan M.; Robles Gómez, Antonio; Paoletti, Mercedes E.; Benítez Andrades, José Alberto; https://orcid.org/0000-0002-4089-9538Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT) remains the primary diagnostic tool in stroke emergencies due to its speed, accessibility, and cost-effectiveness. NCCT enables the exclusion of hemorrhage and directs attention to ischemic causes resulting from arterial flow obstruction. Quantification of NCCT findings employs the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which evaluates affected brain structures. This study seeks to identify early alterations in NCCT density in patients with stroke symptoms using a binary classifier distinguishing NCCT scans with and without stroke. To achieve this, various well-known deep learning architectures, namely VGG3D, ResNet3D, and DenseNet3D, validated in the ImageNet challenges, are implemented with 3D images covering the entire brain volume. The training results of these networks are presented, wherein diverse parameters are examined for optimal performance. The DenseNet3D network emerges as the most effective model, attaining a training set accuracy of 98% and a test set accuracy of 95%. The aim is to alert medical professionals to potential stroke cases in their early stages based on NCCT findings displaying altered density patterns.Publicación Researchers’ 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) González-Blanco García, Elena; Rio Riande, Gimena del; Robles Gómez, Antonio; Ros Muñoz, Salvador; Hernández Berlinches, Roberto; Tobarra Abad, María de los Llanos; Caminero Herráez, Agustín Carlos; Pastor Vargas, RafaelPublicación Machine learning models and dimensionality reduction for improving the Android malware detection(PeerJ, 2024-12-23) Moran, Pablo; Robles Gómez, Antonio; Duque Fernández, Andrés; Tobarra Abad, María de los Llanos; Pastor Vargas, RafaelToday, a great number of attack opportunities for cybercriminals arise in Android, since it is one of the most used operating systems for many mobile applications. Hence, it is very important to anticipate these situations. To minimize this problem, the analysis of malware search applications is based on machine learning algorithms. Our work uses as a starting point the features proposed by the DREBIN project, which today constitutes a key reference in the literature, being the largest public Android malware dataset with labeled families. The authors only employ the support vector machine to determine whether a sample is malware or not. This work first proposes a new efficient dimensionality reduction of features, as well as the application of several supervised machine learning algorithms for prediction purposes. Predictive models based on Random Forest are found to achieve the most promising results. They can detect an average of 91.72% malware samples, with a very low false positive rate of 0.13%, and using only 5,000 features. This is just over 9% of the total number of features of DREBIN. It achieves an accuracy of 99.52%, a total precision of 96.91%, as well as a macro average F1-score of 96.99%.Publicación Automated IoT vulnerability classification using Deep Learning(2025-07-01) Enrique Fernández Morales,; García Merino, José Carlos; Tobarra Abad, María de los Llanos; Pastor Vargas, Rafael; Robles Gómez, Antonio; Sarraipa, Joao; Sernández Iglesias, DanielTechnological advancements in the development of low-power chips have enabled everyday objects to connect to the Internet, giving rise to the concept known as the Internet of Things (IoT). It is currently estimated that there are approximately 16 billion IoT connections worldwide, a figure expected to double by 2030. However, this rapid growth of the IoT ecosystem has introduced new vulnerabilities that could be exploited by malicious actors. Since many IoT devices handle personal and sensitive information, threats to these devices can have severe consequences. Moreover, a series of cybersecurity incidents could undermine public trust in IoT technology, potentially delaying its widespread adoption across various sectors.Common Vulnerabilities and Exposures records (also known by their acronym as CVEs) is a public cataloging system designed to identify and list known security vulnerabilities in software and hardware products. This system is developed and maintained by MITRE with the support of the cybersecurity community and sponsored by the U.S. Department of Homeland Security (DHS) through the Cybersecurity and Infrastructure Security Agency (CISA). CVE provides a reference database that enables security researchers, manufacturers, and organizational security managers to more effectively identify and address security issues.In our study, we have focused on CVEs exclusively oriented towards IoT systems, with the aim of analyzing the main vulnerabilities detected from 2010 to nowadays as a basis for detecting the main attack vectors in IoT systems. As part of this effort we have created the following dataset. CVEs records include various metrics such as: - Common Weakness Enumeration (CWE), mainly focused on technical classification of vulnerabilities. - Common Vulnerability Scoring System (CVSS), which reports about different metrics such as the attack vector, the severity of the vulnerability or the impact level of the exploitation of the vulnerability. This is one of the most informative metric. - Stakeholder-Specific Vulnerability Categorization (SSVC), oriented towards help cybersecurity team to handle properly the vulnerability. These metrics allow security teams on the one hand to prioritize, such vulnerabilities within their security program, evaluating efforts to mitigate them. But according to our analysis of our dataset, around the 14% of CVEs records do not contain any metric. Around the 83% of CVEs registries contain CWE metric (an ID or its textual description). This metric, as it is explained before, only reports about the type of vulnerability from a technic point of view. Only the 10% of CVEs registries contain SSVC metrics. And CVSS, in its different versions, appears only in the 40% of the studied CVEs registries. Additionally, most of studied records includes metrics a retrospectively, several weeks or months later the vulnerability is disclosed. Thus, cybersecurity teams must trust their previous knowledge in order to distinguish which vulnerabilities are relevant and which not.To tackled this situation, our proposal is focused in the application of Deep Learning techniques in order to classify the severity of CVE records from its textual description. Textual description is a mandatory field that is present in all CVEs records. To achieve this objective, we trained the BiLSTM algorithm using the CVE records with CVSS metrics and its description field; and performed a comparative study of different hyperparameter configurations to find the optimal configuration. The metrics for model evaluation that have been studied are accuracy, loss and F1-score.Publicación On the optimal selection of Mel-Frequency Cepstral Coefficients for voice deepfake detection(Wiley, 2026-03-24) Falcón López, Sergio A.; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, RafaelThe continuous evolution of techniques for generating manipulated audio, known as voice deepfakes, and the widespread availability of tools that produce convincing forgeries have created an urgent need for reliable detection methods. This work considers the dimensionality of Mel-Frequency Cepstral Coefficients (MFCCs) as a core design variable for practical, deployable systems. The aim is to identify the smallest number of coefficients that preserves detection performance across heterogeneous models while reducing computational cost, a critical factor for mobile and edge deployment. This study evaluates a hybrid setting on the ASVspoof 2019 Logical Access dataset, in which the same feature family serves as input to five traditional machine learning algorithms (Random Forest, k-Nearest Neighbors, Linear Support Vector Classification, Extreme Gradient Boosting and Support Vector Machine with radial basis function kernel) and five deep learning models (Convolutional Neural Network, Recurrent Neural Network, Convolutional Recurrent Neural Network, Xception and ResNet). Results indicate that deep models reach near-peak performance with a small number of coefficients, whereas classical methods require a larger number to achieve stable performance (except Linear Support Vector Classification, which consistently underperforms). Accordingly, 32 coefficients are considered an effective operating point for hybrid deployments. Overall, the results provide evidence to guide the selection of the number of MFCC coefficients in voice deepfake detection, aiming for efficient, reproducible and explainable systems.Publicación EVI-LINHD, a virtual research environment for the Spanish speaking community(Oxford University Press, 2017-12) González-Blanco García, Elena; Rio Riande, Gimena del; Díez Platas, María Luisa; Olmo, Álvaro del; Urízar, Miguel; Martínez Cantón, Clara Isabel; Ros Muñoz, Salvador; Pastor Vargas, Rafael; Robles Gómez, Antonio; Caminero Herráez, Agustín CarlosLaboratorio de Innovación en Humanidades Digitales (UNED) has developed Entorno Virtual de Investigación del Laboratorio de Innovación en Humanidades Digitales (EVI-LINHD), the first virtual research environment devoted mainly to Spanish speakers interested in digital scholarly edition. EVI-LINHD combines different open-source software for developing a complete digital project: (1) a Webbased application markup tool—TEIscribe—combined with an eXistdb solution and a TEIPublisher platform, (2) Omeka for digital libraries, and (3) WordPress for simple Web pages. All these instances are linked to a local installation of the LINDAT/Common Language Resources and Technology Infrastructure (CLARIN) digital repository. LINDAT/CLARIN allows EVI-LINHD users to have their projects deposited and stored safely. Thanks to this solution, EVI-LINHD projects also improve their visibility. The specific metadata profile used in the repository is based on Dublin Core, and it is enriched with the Spanish translation of DARIAH’s Taxonomy of Digital Research Activities in the Humanities.