Sobel filter and linear classification for deepfake analysis of faces

  • Fernanda G. Tamanaka Centro Universitário FEI
  • Carlos E. Thomaz Centro Universitário FEI

Resumo


Por meio de técnicas de inteligência artificial (IA) são criadas deepfakes. Atualmente, quase seis anos após a popularização desse tema, algumas pessoas estão utilizando algoritmos de IA para alterar, por exemplo, o rosto. Neste cenário, este artigo tem como proposta aplicar o filtro Sobel, em conjunto com técnicas multivariadas de aprendizagem estatística, para a identificação das características mais discriminantes. Complementarmente, visamos analisar as áreas que humanos visualmente identificaram como relevantes para detectar uma deepfake. Os resultados mostram que o modelo estatístico (PCA mais MLDA) combinado com o filtro Sobel classificou corretamente a maioria das imagens, realçando as regiões que discriminam uma deepfake.
Palavras-chave: Deepfakes, Sobel, PCA, Rastreamento ocular

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Publicado
25/09/2023
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TAMANAKA, Fernanda G.; THOMAZ, Carlos E.. Sobel filter and linear classification for deepfake analysis of faces. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 15-27. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2023.233505.