Anonimização facial com Inteligência Artificial na Musicoterapia: um estudo piloto com o AKOOL Face Swap e o Syntonym

Autores

DOI:

https://doi.org/10.55777/rea.v19i37.8603

Palavras-chave:

Musicoterapia, Anonimización facial, Inteligencia Artificial, Expresión facial, Privacidad

Resumo

Na musicoterapia, a partilha de materiais clínicos exige um equilíbrio entre a proteção da privacidade e a preservação da informação emocional, uma vez que métodos tradicionais, como o desfoque, podem ocultar expressões faciais e reduzir o valor clínico do material. Este estudo piloto envolveu 20 musicoterapeutas que avaliaram três excertos de vídeo apresentados em quatro versões: original, desfocada e com substituição facial, utilizando duas aplicações de inteligência artificial (AKOOL Face Swap e Syntonym). As classificações foram recolhidas utilizando escalas do tipo Likert e analisadas com procedimentos estatísticos, juntamente com estimativas de fiabilidade e concordância entre avaliadores. Os resultados revelaram diferenças significativas (χ²(2)=13,30; p=0,0013), com melhorias significativas na utilidade clínica e na expressividade das versões baseadas em IA em comparação com o desfoque (≈0,8–0,9 pontos; p<0,01), e sem diferenças entre o AKOOL e o Syntonym. A fiabilidade variou de aceitável a excelente (α=0,61–0,88; ω=0,79–0,91), e a concordância entre avaliadores foi elevada quando calculada em média entre os avaliadores (ICC=0,86). A substituição de rostos baseada em IA preserva melhor os sinais expressivos e proporciona um equilíbrio mais favorável entre privacidade e utilidade do que o desfoque, tornando-a adequada para investigação e divulgação pública sob salvaguardas éticas e regulamentares apropriadas. No entanto, os materiais originais continuam a ser mais adequados para contextos de ensino e supervisão fechada, com o consentimento necessário.

Downloads

Não há dados estatísticos.

Referências

Abdulaziz, S., & Bondarev, E. (2025). Unmasking performance gaps: A comparative study of human anonymization and its effects on video anomaly detection (arXiv:2507.14083). arXiv. https://doi.org/10.48550/arXiv.2507.14083

Agarwal, S., Peta, S., & Panyam, S. (2024). Deepfakes in healthcare: Reviewing the transformation potential and its challenges. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 3965–3970.

Akool. (n.d.). Best face swap for photos & videos online with free trial. Akool. https://akool.com/apps/faceswap

Amorós-Sánchez, B., Gamella-González, D. J., Cisneros-Álvarez, P., & Gisbert-Caudeli, V. (2024). A systematic review of the technology available for data collection and assessment in music therapy. En A. L. Brooks (Ed.), ArtsIT 2023… Proceedings, Part I (pp. 41–54). Springer. https://doi.org/10.1007/978-3-031-55319-6_4

American Music Therapy Association. (n.d.). Code of ethics. https://www.musictherapy.org/about/ethics/

American Psychological Association (2024). Proposed Revision of Guidelines for the Practice of Telepsychology. Tomado de: https://www.apa.org/practice/guidelines/telepsychology-revisions.pdf

Baumgartner, R., Arora, P., Bath, C., Williams, R. (2023). Fair and equitable AI in biomedical research and healthcare: Social science perspectives. Artificial Intelligence in Medicine, 144, 102658. https://doi.org/10.1016/j.artmed.2023.102658

Behrens, G. A. (2020). Considerations when writing and presenting consent forms for clients. Music Therapy Perspectives, 38(1), 38–41. https://doi.org/10.1093/mtp/miz029

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806

Bruscia, K. E. (2014). Defining music therapy (4th ed.). Barcelona Publishers.

Bunt, L., & Stige, B. (2014). Music therapy: An art beyond words (2.ª ed.). Routledge.

Chesney, R., & Citron, D. (2019). Deepfakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107, 1753–1819. https://doi.org/10.2139/ssrn.3213954

Clements-Cortés, A., Fuller, A., Kelly, L., Pranjić, M., Selvarajah, I., Mercadal-Brotons, M., & Bridi, N. (2025). Music therapists’ global perspectives on telehealth music therapy: A qualitative interview inquiry. Music Therapy Perspectives. Advance online publication. https://doi.org/10.1093/mtp/miae030

Conduah, A. K., Ofoe, S., & Siaw-Marfo, D. (2025). Data privacy in healthcare: Global challenges and solutions. Digital Health, 11, 20552076251343959. https://doi.org/10.1177/20552076251343959

Creswell, J. W., & Plano Clark, V. L. (2014). Designing and conducting mixed methods research (2.ª ed.). SAGE.

Dileo, C. (2007). Ethical thinking in music therapy. Jeffrey Books.

Fattorini Vaca, A., & Gamella-González, D. J. (2022). Ética profesional en musicoterapia desde la perspectiva del musicoterapeuta. En S. Olivero Guidobono (Coord.), Artes y humanidades en el centro de los conocimientos: Miradas sobre el patrimonio, la cultura, la historia, la antropología y la demografía (pp. 637–670). Dykinson. https://www.dykinson.com/libros/artes-y-humanidades-en-el-centro-de-los-conocimientos-miradas-sobre-el-patrimonio-la-cultura-la-historia-la-antropologia-y-la-demografia/9788413779263/

Fernández-Company, J. F., Gamella-González, D. J., & García-Rodríguez, M. (2024). Autoevaluación de la práctica en musicoterapia para el crecimiento profesional. En M. del M. Simón Márquez, P. Molina Moreno, J. J. Gázquez Linares, & S. Fernández Gea (Coords.), Innovación en salud: Estrategias emergentes para la docencia y la investigación (pp. 27–33). ASUNIVEP. https://dialnet.unirioja.es/servlet/libro?codigo=974646

Fisher, C. B. (2021). Decoding the ethics code: A practical guide for psychologists (5th ed.). SAGE.

Flores Cruz, R. (2025). Uso de la realidad virtual para afrontar obstáculos didácticos, atendiendo los principales estilos de aprendizaje. Revista de Estilos de Aprendizaje, 18(35). Recuperado de https://revistaestilosdeaprendizaje.com/index

Franzreb, C., Das, A., Gieseler, H., Jahn, E. C., Polzehl, T., & Möller, S. (2024). Towards audiovisual anonymization for remote psychotherapy: A subjective evaluation. En Proceedings of SPSC 2024 (pp. 102–110). ISCA. https://doi.org/10.21437/SPSC.2024-17

Gamella-González, D. J. (2025). Proyecto MUTia: Inteligencia artificial aplicada al songwriting en musicoterapia. En D. De la Rosa & P. Renés (Eds.), Políticas educativas y nuevos modelos de enseñanza-aprendizaje en educación superior y formación profesional (pp. 229–252). Tirant lo Blanch.

Geretsegger, M., Elefant, C., Mössler, K. A., & Gold, C. (2014). Music therapy for people with autism spectrum disorder. Cochrane Database of Systematic Reviews, 2014(6), CD004381. https://doi.org/10.1002/14651858.CD004381.pub3

Gold, C. (2016). Abstracts of the 10th European Music Therapy Conference. Nordic Journal of Music Therapy, 25(sup1), 1–156. https://doi.org/10.1080/08098131.2016.11783620

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks. arXiv. https://doi.org/10.48550/arXiv.1406.2661

Gupta, A., Malik, P., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328–2331. https://doi.org/10.4103/jfmpc.jfmpc_440_19

Hamilton, L. (2025). The future of music therapy: An exploration of music therapists’ perceptions of artificial intelligence and its ethical implications [Trabajo fin de grado, Edinburgh College of Art, Universidad de Edimburgo]. Edinburgh College of Art Graduate Show. https://www.2025.graduateshow.eca.ed.ac.uk/sites/default/files/2025-05/LH%20Dissertation%20ECA%20Graduate%20Show%202025.pdf

Hewson, T., Abraham, S., Randles, N., Akinola, A., Cliff, R., Byrne, P., & Ramkisson, R. (2022). The recording of mental health consultations by patients: Clinical, ethical and legal considerations. BJPsych Bulletin, 46(3), 133–137. https://doi.org/10.1192/bjb.2021.89

Hoek S, Metselaar S, Ploem C. & Bak, C. (2025). Promising for patients or deeply disturbing? The ethical and legal aspects of deepfake therapy. Journal of Medical Ethics 51:481-486. https://doi.org/10.1136/jme-2024-109985

Johnson, J., Alahi, A., & Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. En ECCV 2016 (pp. 694–711). Springer. https://doi.org/10.1007/978-3-319-46475-6_43

Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. En CVPR 2019 (pp. 4401–4410). IEEE. https://doi.org/10.1109/CVPR.2019.00453

Kern, P. (2025). Artificial intelligence in music therapy: A new era of personalized care and scalable impacts. MiSOSTENiDO, 5(9), 8–16. https://doi.org/10.59028/misostenido.2025.02

Khalid, H., Kim, M., Tariq, S., & Woo, S. S. (2021). Evaluation of an audio-video multimodal deepfake dataset using unimodal and multimodal detectors. En ADGD ’21, ACM MM Workshops (pp. 7–15). ACM. https://doi.org/10.1145/3476099.3484315

Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158, 106848. https://doi.org/10.1016/j.compbiomed.2023.106848

Kim, J., & Lee, S. (2018). Music therapy for depression: A meta-analysis. The Arts in Psychotherapy, 59, 103–112. https://doi.org/10.1016/j.aip.2018.03.004

Lavanchy, J. L., Vardazaryan, A., Mascagni, P., et al. (2023). Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos. Scientific Reports, 13, 9235. https://doi.org/10.1038/s41598-023-36453-1

Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. En ICCV 2015 (pp. 3730–3738). IEEE. https://doi.org/10.1109/ICCV.2015.425

Navarro Martínez, O., Fernández-García, D., Cuartero Monteagudo, N., & Forero-Rincón, O. (2024). Possible health benefits and risks of deepfake videos: A qualitative study in nursing students. Nursing Reports, 14(4), 2746–2757. https://doi.org/10.3390/nursrep14040203

NHS Lothian. (2023). Audio and video recordings of psychological therapy sessions policy. https://policyonline.nhslothian.scot/wp-content/uploads/2023/03/Audio-and-Video-Recordings-of-Psychological-Therapy-Sessions-Policy.pdf

Paris, B., & Donovan, J. (2019). Deepfakes and cheap fakes: The manipulation of audio and visual evidence. Data & Society. https://datasociety.net/library/deepfakes-and-cheap-fakes/

Parlamento Europeo & Consejo de la Unión Europea. (2016). Regulation (EU) 2016/679 (GDPR). Official Journal of the European Union, L119, 1–88. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32016R0679

Pérez, M., & García, J. (2025). Adaptación y validación de la escala TPACK en la formación de profesores para la educación a distancia. Revista de Estilos de Aprendizaje, 18(35). Recuperado de https://revistaestilosdeaprendizaje.com/index

Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital ethnography: Principles and practice. SAGE.

Qureshi, J., & Khan, S. (2025). Artificial intelligence deepfakes in healthcare systems: A double-edged sword? International Journal of Data Science and Big Data Analytics, 5(1), 84–93. https://doi.org/10.51483/IJDSBDA.5.1.2025.84-93

Reid, A., & Miño, P. (2021). When therapy goes public: Copyright gatekeepers and sharing therapeutic artifacts on social media. International Journal of Communication, 15, 950–969. https://ijoc.org/index.php/ijoc/article/view/1652

Sæle, R. G., & Gilbertson, S. (2024). Ethical reflections on social media use within music therapy: A conversational focus group study. Nordic Journal of Music Therapy, 33(5), 409–425. https://doi.org/10.1080/08098131.2024.2350939

Shuvi, M., Fish, N., Aberman, K., Shamir, A., & Cohen-Or, D. (2020). Neural alignment for face de-pixelization (arXiv:2009.13856). arXiv. https://doi.org/10.48550/arXiv.2009.13856

Sun, J., Yang, J., Zhou, G., Jin, Y., & Gong, J. (2024). Understanding human-AI collaboration in music therapy through co-design with therapists. En CHI 2024 Proceedings (Art. 704, pp. 1–21). ACM. https://doi.org/10.1145/3613904.3642764

Syntonym. (n.d.). Generative AI for privacy! Hyper realistic face anonymization for videos & images. Syntonym. https://www.Syntonym.com/

Tariq, S., Woo, S. S., Singh, P., Irmalasari, I., Gupta, S., & Gupta, D. (2025). From prediction to explanation: Multimodal, explainable, and interactive deepfake detection framework for non-expert users (arXiv:2508.07596) https://arxiv.org/abs/2508.07596

Tiribelli, S., Monnot, A., Shah, S. F. H., Arora, A., Toong, P. J., & Kong, S. (2023). Ethics Principles for Artificial Intelligence-Based Telemedicine for Public Health. American journal of public health, 113(5), 577–584. https://doi.org/10.2105/AJPH.2023.307225

Todt, J., Hanisch, S., & Strufe, T. (2024). Fantômas: Understanding face anonymization reversibility. Proceedings on Privacy Enhancing Technologies, 2024(4), 24–43. https://doi.org/10.56553/POPETS-2024-0105

Warrier, U., Warrier, A., & Khandelwal, K. (2023). Ethical considerations in the use of artificial intelligence in mental health. The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 59, 139. https://doi.org/10.1186/s41983-023-00735-2

Westerlund, M. (2019). The emergence of deepfake technology: A review. Technology Innovation Management Review, 9(11), 39–52. https://doi.org/10.22215/timreview/1282

Yim, D., Khuntia, J., Parameswaran, V., & Meyers, A. (2024). Preliminary evidence of the use of generative AI in health care clinical services: Systematic narrative review. JMIR Medical Informatics, 12, e52073. https://doi.org/10.2196/52073

Zhu, Y., Imoussaïne-Aïkous, M., Côté-Lussier, C., and H. Falk, T. (2024). On the Impact of Voice Anonymization on Speech Diagnostic Applications: A Case Study on COVID-19 Detection. Trans. Info. For. Sec. 19 5151–5165. https://doi.org/10.1109/TIFS.2024.3390990

PORTADO_VOLUMEN 19, NÚMERO 37

Publicado

2026-04-15

Como Citar

Amorós Sánchez, B., & Gamella González, D. J. (2026). Anonimização facial com Inteligência Artificial na Musicoterapia: um estudo piloto com o AKOOL Face Swap e o Syntonym. Revista De Estilos De Aprendizagem, 19(37), 44–58. https://doi.org/10.55777/rea.v19i37.8603

Edição

Secção

ARTÍCULOS DE INVESTIGACIÓN TEMÁTICA. Volumen 19, Número 37 (Abril, 2026). Educando en la diversidad mediante propuestas musicales y artísticas