Implementación de herramientas de Inteligencia Artificial en la detección de vídeos falsos y ultrafalsos (deepfakes)Caso de Radio Televisión Española (RTVE)

  1. Sánchez Esparza, Marta
  2. Palella Stracuzzi, Santa
  3. Fernández Fernández, Ángel
Revista:
VISUAL REVIEW: International Visual Culture Review / Revista Internacional de Cultura Visual

ISSN: 2695-9631

Año de publicación: 2024

Volumen: 16

Número: 4

Tipo: Artículo

DOI: 10.62161/REVVISUAL.V16.5303 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: VISUAL REVIEW: International Visual Culture Review / Revista Internacional de Cultura Visual

Resumen

Concerns about the spread of false information have led media outlets to employ artificial intelligence (AI) to detect deepfakes. This research is descriptive-exploratory, a literature review and interviews were conducted. It reveals the transformative impact of AI by highlighting its use to verify the authenticity of content. In this area, RTVE combines traditional methodologies with others based on AI, and leads the development of several tools in collaboration with several universities. These tools have already yielded satisfactory results in the detection of these materials, strengthening the veracity of the information and increasing public confidence in their contents.

Referencias bibliográficas

  • Aramburú, L. G., López, I. y López, A. (2023) «Inteligencia artificial en RTVE al servicio de la España vacía. Proyecto de cobertura informativa con redacción automatizada para las elecciones municipales de 2023», Revista Latina de Comunicación Social, (81), pp. 1–16. doi: 10.4185/RLCS-2023-1550. DOI: https://doi.org/10.4185/RLCS-2023-1550
  • Bañuelos, J. (2022). Evolución del Deepfake: campos semánticos y géneros discursivos (2017-2021). Revista ICONO 14. Revista Científica De Comunicación Y Tecnologías Emergentes, 20(1). https:// doi.org/10.7195 /ri14.v20i1.1773 DOI: https://doi.org/10.7195/ri14.v20i1.1773
  • Branislav, S., Aleksandra, P. (2022). Use of artificial intelligence for the generation of media content. Available from: 10.58898/sij.v1i1.01-07
  • Bouter, M. D. L. D., Pardo, J. L., Geradts, Z., & Worring, M. (2023). ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinement. arXiv preprint arXiv:2309.11155. DOI: https://doi.org/10.1177/14738716241238476
  • Cao, J., Qi, P., Sheng, Q., Yang, T., Guo, J., y Li, J. (2020). Exploring the Role of Visual Content in Fake News Detection. In K. Shu, S. Wang, D. Lee y H. Liu (ed.). Disinformation, Misinformation, and Fake News in Social Media: Emerging Research Challenges and Opportunities (pp. 141-161). Springer International Publishihaidng. DOI: https://doi.org/10.1007/978-3-030-42699-6_8
  • Cerdán, V. y Padilla, G. (2019). Historia del fake audiovisual: deepfake y la mujer en un imaginario falsificado y perverso. Historia y comunicación social, 24(2). https://dx.doi.org/10.5209/hics.66293 DOI: https://doi.org/10.5209/hics.66293
  • Colin, P. (2023). Promoting responsible AI: A European perspective on the governance of artificial intelligence in media and journalism. Communications. 10.1515/commun-2022-0091
  • Eva, K. á(2022). Usage of artificial intelligence on social media in europe. Ad alta, Available from: 10.33543/1202330333
  • Fernández, A. (2017). Relatos híbridos: El papel de la narratividad en la visualización de información interactiva [Tesis doctoral, Universidad Europea]. Repositorio Abacus https://193.147.239.238/handle/11268/6981
  • Fernández, A., Revilla, A. y Andaluz, L. (2020). Análisis de la caracterización discursiva de los relatos migratorios en Twitter. El caso Aquarius. Revista Latina de Comunicación Social, (77), 1-18. https://doi.org/10.4185/RLCS-2020-1446 DOI: https://doi.org/10.4185/RLCS-2020-1446
  • Guarnera, L., & Battiato, S. (2023). An Overview of Deepfake Technologies: from Creation to Detection in Forensics.
  • Haidar, H. (2023). Using artificial intelligence to verify media content on the Internet" A survey study of journalists working in Iraqi media institutions. International Journal of Media Studies and Communication Sciences. 10.36772/arid.aijmscs.2023.485
  • Hameleers, M., Powell, T. E., Van der Meer, T. y Bos, L. (2020). A picture paints a thousand lies? The effects and mechanisms of multimodal disinformation and rebuttals disseminated via social media. Political Communication, 37 (2), 281-301. DOI: https://doi.org/10.1080/10584609.2019.1674979
  • Haseena, S., Saroja, S., Nivetha, A. (2023). TVN: Detect Deepfakes Images using Texture Variation Network. Inteligencia artificial, doi: 10.4114/intartif.vol26iss72pp1-14 DOI: https://doi.org/10.4114/intartif.vol26iss72pp1-14
  • Jankowicz, N., Hunchak, J., Pavliuc, A., Davies, C., Pierson, S., y Kaufmann, Z. (2021) Malign Creativity: How Gender, Sex and Lies Are Weaponized Against Women Online, Washington, D.C.: Wilson Center. https://www.wilsoncenter.org/publication/malign-creativity-how-gender-sex-and-lies-are-weaponized-against-women-online
  • Jerónimo, P., & Esparza, M. S. (2022). Disinformation at a Local Level: An Emerging Discussion. Publications, 10(2), 15. MDPI AG. Retrieved from http://dx.doi.org/10.3390/publications10020015 DOI: https://doi.org/10.3390/publications10020015
  • Jing, H.(2023). The Rising Trend of Artificial Intelligence in Social Media. Advances in computer and electrical engineering book series, Available from: 10.4018/978-1-6684-6937-8.ch003
  • Kalin, S., Bhawna, P., Dhall, A.. (2022). Visual Representations of Physiological Signals for Fake Video Detection. arXiv.org. 10.48550/arXiv.2207.08380
  • Karnouskos, S. (2020). Artificial intelligence in digital media: The era of deepfakes in IEEE Transactions on Technology and Society, 1(3), 138-147. doi: 10.1109/TTS.2020.3001312 DOI: https://doi.org/10.1109/TTS.2020.3001312
  • Kavanagh, J. y Rich, M. D. (2018). Truth decay: An initial exploration of the diminishing role of facts and analysis in American public life, Rand Corporation. DOI: https://doi.org/10.7249/RR2314
  • Koldobika, Meso-Ayerdi., Larrondo-Ureta, A.., Díaz-Noci, J. (2023). Without journalists, there is no journalism: the social dimension of generative artificial intelligence in the media. Profesional De La Informacion, Available from: 10.3145/epi.2023.mar.27
  • Liz-López, H. ; Keita M. , Taleb-Ahmed, A. Abdenour H. , Huertas-Tato, J. , Camacho D. , “Generación y detección de contenidos audiovisuales multimodales manipulados: Avances, tendencias y desafíos abiertos”, Fusión de Información, pp.102103, 2023. DOI:10.1109/wacvw58289.2023.00071 DOI: https://doi.org/10.1016/j.inffus.2023.102103
  • Martin-Rodriguez, F., Garcia-Mojon, R. & Fernandez-Barciela, M. (2023). Detection of AI-Created Images Using Pixel-Wise Feature Extraction and Convolutional Neural Networks. Sensors. [Online]. 23 (22). p.p. 9037. Available from: http://dx.doi.org/10.3390/s23229037. Mathias, F. , de-Lima-Santos., W. , Ceron, A.. (2021). Artificial Intelligence in News Media: Current Perceptions and Future Outlook. Available from: 10.20944/PREPRINTS202110.0020.V1 DOI: https://doi.org/10.3390/s23229037
  • Matthew, N., O., Sadiku., Tolulope, J., Ashaolu., Abayomi, Ajayi-Majebi., Sarhan, M., Musa. (2021). Artificial Intelligence in Social Media. Available from: 10.51542/IJSCIA.V2I1.4
  • Nandini, S., Akshay, B, G., Brunda, A, N., Chandana, A, M., Divyashree, S, R. (2022). Advanced Reverse Image Search and Profile Creation using Machine Learning. International Journal of Advanced Research in Science, Communication and Technology. 10.48175/ijarsct-5417
  • Olmo, J. y Romero, A. (2019). Desinformación: Concepto y perspectivas. Análisis del Real Instituto Elcano (ARI), (41). https://www.realinstitutoelcano.org/analisis/desinformacion-concepto-y-perspectivas/
  • Palella, S y Martins, F. (2017). Metodología de la investigación cuantitativa. FEDEUPEL
  • Pineda, A. (2004). Más allá de la historia: aproximación a los elementos teóricos de la propaganda de guerra. En A. Pena (Ed.), Comunicación y guerra en la historia (pp. 807-823). Santiago de Compostela: Tórculo http://hdl.handle.net/11441/64448
  • Rashmi, C., Bhargavi, V., Samhitha, S., Anjana, Y., & Saivaishnavi, V. (2023). Fake detect: a deep learning ensemble model for fake news detection (ml). Turkish Journal of Computer and Mathematics Education (TURCOMAT), 14(03), 684-688.
  • Shilpa, B., Anush, Kamath., Hemanth, Bhat., Sathwik, A, M. (2023). Unmasking Deepfakes: Using Resnext and LSTM to Detect Deepfake Videos. International Journal of Advanced Research in Science, Communication and Technology, doi: 10.48175/ijarsct-8639 DOI: https://doi.org/10.48175/IJARSCT-8639
  • Sohrawardi, S., Chintha, A., Thai, B., Seng, S., Hickerson, A., Ptucha, R. y Wright, M. (2019). Póster: Hacia una detección sólida de deepfakes en mundo abierto. Actas de la Conferencia ACM SIGSAC de 2019 sobre seguridad informática y de las comunicaciones. https://doi.org/10.1145/3319535.3363269 DOI: https://doi.org/10.1145/3319535.3363269
  • Taylor, M. (2023). Deepfakes, Fake Barns, and Knowledge from Videos. Synthese, Available from: 10.1007/s11229-022-04033-x
  • Timothy, K., Shih. A. (2011). Video Forgery. Available from: 10.1109/NBIS.2011.120 [3]
  • Vedamurthy, H., (2022). A reliable solution to detect deepfakes using Deep Learning. doi: 10.1109/CCIP57447.2022.10058638 DOI: https://doi.org/10.1109/CCIP57447.2022.10058638
Fuente de los datos: Dialnet