DEEPFAKES Y PRUEBA DIGITAL EN LA VIOLENCIA DOMÉSTICA: LOS RIESGOS DE LA INTELIGENCIA ARTIFICIAL

Autores/as

  • Pedro Miguel Freitas Universidade Católica Portuguesa
  • Ana Guerreiro University of Maia (UMaia)

DOI:

https://doi.org/10.14210/nej.v30n3.p398-419

Palabras clave:

Prueba audiovisual, valoración de la prueba, deepfakes, autenticidad, inteligencia artificial

Resumen

Contextualización: Los avances recientes de la inteligencia artificial generativa han introducido nuevos desafíos al sistema de justicia penal, particularmente en el ámbito de la prueba digital. Entre las aplicaciones más problemáticas destacan los deepfakes, susceptibles de inducir a error en cuanto a su autenticidad, especialmente en contextos de mayor fragilidad probatoria, como los delitos de violencia doméstica.

Objetivos: El presente estudio tiene como objetivo general comprender las percepciones, experiencias y prácticas de los magistrados en relación con la admisibilidad y valoración de la prueba digital, con especial enfoque en los medios de prueba audiovisual y en los riesgos asociados a su eventual manipulación por tecnologías de inteligencia artificial generativa.

Método: Se adoptó un enfoque cualitativo, recurriendo a la realización de entrevistas semiestructuradas a ocho magistrados con experiencia profesional en procesos de violencia doméstica

Resultados: Se observaron limitaciones en la detección de elementos audiovisuales generados o manipulados por inteligencia artificial. La autenticidad de este tipo de elementos (deepfakes) ha granjeado más credibilidad que los elementos auténticos. De todos modos, aunque la prueba audiovisual se considera relevante, el testimonio de la víctima sigue considerándose la «prueba reina».

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Biografía del autor/a

Pedro Miguel Freitas, Universidade Católica Portuguesa

Investigador do Católica Research Centre for the Future of Law (CEID) - Portugal. Doutor pela Escola de Direito da Universidade do Minho - Portugal. Mestre pela Escola de Direito da Universidade do Minho - Portugal. ORCID: 0000-0003-4516-0588.

Ana Guerreiro, University of Maia (UMaia)

PhD in Criminology from the Faculty of Law, University of Porto. Assistant Professor at the University of Maia and Invited Assistant Professor at the Faculty of Law, School of Criminology, University of Porto (Portugal). Researcher at both the Interdisciplinary Centre for Gender Studies, Institute of Social and Political Sciences, University of Lisbon (CIEG-ISCSP, UL), and the CIJ – Centre for Interdisciplinary Research on Justice, University of Porto (Portugal). ORCID: 0000-0003-2312-6266.

Citas

BELCIC, Ivan; STRYKER, Cole. What is GPT (generative pretrained transformer)?. IBM, [S.l.], [S.d.]. Disponível em: https://www.ibm.com/think/topics/gpt. Acesso em: 03 dez. 2025.

BRAUN, Virginia; CLARKE, Victoria. Thematic analysis. In: COOPER, Harris et al. (Org.). APA handbook of research methods in psychology. v. 2. Washington, DC: American Psychological Association, 2012. p. 57–71.

BRAUN, Virginia; CLARKE, Victoria. Using thematic analysis in psychology. Qualitative Research in Psychology, London, v. 3, n. 2, p. 77–101, 2006.

BRINKMANN, Steinar. The interview. In: DENZIN, Norman K.; LINCOLN, Yvonna S. (Org.). The SAGE handbook of qualitative research. 5. ed. Thousand Oaks: SAGE Publications, 2018. p. 997-1038.

CHESNEY, Robert; CITRON, Danielle K. Deep Fakes: A looming challenge for privacy, democracy, and national security. California Law Review, v. 107, n. 6, p. 1771-1786, 2019.

CHINA, Chrystal R. Five machine learning types to know. In: Cole STRYKER et al. (eds.). The 2025 Guide to Machine Learning. [S.l.]: IBM, 2025. Disponível em: https://www.ibm.com/think/topics/machine-learning-types. Acesso em: 01 nov. 2025.

COLLINS DICTIONARY. Collins Online Dictionary: definitions, thesaurus and translations. Disponível em: https://www.collinsdictionary.com. Acesso em: 03 dez. 2025.

COMISSÃO EUROPEIA. Comunicação da Comissão ao Parlamento Europeu, ao Conselho, ao Comité Económico e Social Europeu e ao Comité das Regiões: Roteiro para o acesso lícito e efetivo aos dados para efeitos da aplicação da lei. Bruxelas, 2025.

DIXON JR., Herbert B. DeepFakes: More frightening than photoshop on steroids. Judges’ Journal, v. 58, n. 3, p. 35-37, 2019.

DURÃES, Dalila; FREITAS, Pedro Miguel; NOVAIS, Paulo. The Relevance of Deepfakes in the Administration of Criminal Justice. In: ANTUNES, Henrique Sousa et al. (eds). Multidisciplinary Perspectives on Artificial Intelligence and the Law. Cham: Springer, 2024.

EDMONDS, Lauren. 3 years old, 800 million users, 29,000 prompts a second: ChatGPT's meteoric rise, by the numbers”. Business Insider, [S.l.], 2025. Disponível em: https://www.businessinsider.com/chatgpt-by-the-numbers-2025-11#chat-gpt-has-800-million-weekly-active-users-2. Acesso em: 03 dez. 2025.

EUROPOL. Facing reality? Law enforcement and the challenge of deepfakes. Luxemburgo: Publications Office of the European Union, 2022.

GAGLIARDI, Marília Papaléo. Artificial Intelligence and Women’s Rights. In: QUINTAVALLA, Alberto e TEMPERMAN, Jeroen (eds.). Artificial Intelligence and Human Rights. Oxford: OUP Oxford, 2023.

GOOGLE. What is machine learning (ML)?. Google, [S.l.], [S.d.]. Disponível em: https://cloud.google.com/learn/what-is-machine-learning?hl=en. Acesso em: 03 dez. 2025.

GOOGLE. What is Machine Learning? In: GOOGLE. Introduction to Machine Learning. [S.l.], 2025. Disponível em: https://developers.google.com/machine-learning/intro-to-ml/what-is-ml. Acesso em: 27 nov. 2025.

LEE, Fangfang. What is a neural network? In: Cole STRYKER et al. (eds.). The 2025 Guide to Machine Learning. [S.l.]: IBM, 2025. Disponível em: https://www.ibm.com/think/topics/neural-networks. Acesso em: 30 nov. 2025.

LEVY, Neil. Bad Beliefs: Why They Happen to Good People. Oxford: Oxford University Press, 2022.

MARAS, Marie-Helen; ALEXANDROU, Alex. Determining authenticity of video evidence in the age of artificial intelligence and in the wake of Deepfake vídeos. The International Journal of Evidence & Proof, v. 23, n. 3, p. 255-262, 2018

OPENAI. Fine-tuning GPT-2 from human preferences. OpenAI, [S.l.], 2019. Disponível em: https://openai.com/index/fine-tuning-gpt-2/. Acesso em: 03 dez. 2025.

OPENAI. Introducing ChatGPT. OpenAI, [S.l.], 2022. Disponível em: https://openai.com/index/chatgpt/. Acesso em: 03 dez. 2025.

OPENAI. OpenAI API. OpenAI, [S.l.], 2020. Disponível em: https://openai.com/index/openai-api/. Acesso em: 03 dez. 2025.

RENNISON, Callie Marie; HART, Thomas C. Research methods in criminal justice and criminology. Thousand Oaks: SAGE Publications, 2019.

SCHREIER, Margrit. Sampling and generalization. In: FLICK, Uwe. (Org.). The SAGE handbook of qualitative data collection. London: SAGE Publications, 2018, p. 84-98.

SOR, Jennifer. Sam Altman touts ChatGPT's 800 million weekly users, double all its main competitors combined. Business Insider, [S.l.], 2025. Disponível em: https://www.businessinsider.com/chatgpt-users-openai-sam-altman-devday-llm-artificial-intelligence-2025-10. Acesso em: 03 dez. 2025.

STRYKER, Cole et al. What is deep learning? In: Cole STRYKER et al. (eds.). The 2025 Guide to Machine Learning. [S.l.]: IBM, 2025. Disponível em: https://www.ibm.com/think/topics/deep-learning. Acesso em: 30 nov. 2025.

STRYKER, Cole; SCAPICCHIO, Mark. What is generative AI? In: Cole STRYKER et al. (eds.). The 2025 Guide to Machine Learning. [S.l.]: IBM, 2025. Disponível em: https://www.ibm.com/think/topics/generative-ai. Acesso em: 27 nov. 2025.

SUN, Dawei; BENSON, Michael L. Mixed methods: A justification, explication, and example. In: FARIA, Rita; DODGE, Mary. (Org.). Qualitative research in criminology: Cutting-edge methods. Cham: Springer, 2022. p. 37-49.

TREND MICRO RESEARCH. Malicious Uses and Abuses of Artificial Intelligence. Europol’s European Cybercrime Centre, 2020.

TWOMEY, John et al. What Is So Deep About Deepfakes? A Multi-Disciplinary Thematic Analysis of Academic Narratives About Deepfake Technology. IEEE Transactions on Technology and Society, v. 6, n. 1, p. 64-79, 2025.

UNIVERSIDADE DE HARVARD. The Benefits and Limitations of Generative AI: Harvard Experts Answer Your Questions. Harvard, 2023. Disponível em: https://harvardonline.harvard.edu/blog/benefits-limitations-generative-ai. Acesso em: 27 nov. 2025.

UNIVERSIDADE DE LEEDS. What is Generative AI? University of Leeds, Leeds, [S.d.]. Disponível em: https://generative-ai.leeds.ac.uk/intro-gen-ai/strengths-and-weaknesses/. Acesso em: 27 nov. 2025.

WAGNER, Travis L.; BLEWER, Ashley. The Word Real Is No Longer Real: Deepfakes, Gender, and the Challenges of AI-Altered Video. Open Information Science, v. 3, n. 1, 2019.

WHITTAKER, Lucas et al. Mapping the deepfake landscape for innovation: A multidisciplinary systematic review and future research agenda. Technovation, v. 125, p. 01-17. 2023.

WILES, Rose. What are qualitative research ethics? London: Bloomsbury Academic, 2013.

Publicado

2025-12-19

Cómo citar

FREITAS, Pedro Miguel; GUERREIRO, Ana. DEEPFAKES Y PRUEBA DIGITAL EN LA VIOLENCIA DOMÉSTICA: LOS RIESGOS DE LA INTELIGENCIA ARTIFICIAL. Novos Estudos Jurí­dicos, Itajaí­ (SC), v. 30, n. 3, p. 398–419, 2025. DOI: 10.14210/nej.v30n3.p398-419. Disponível em: https://periodicos.univali.br/index.php/nej/article/view/21857. Acesso em: 18 mar. 2026.

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