The Brazilian Data at Risk in the Age of AI?
Resumo
Com os avanços das técnicas de processamento e análise de imagens, o uso de sistemas de reconhecimento biométrico em tarefas cotidianas das pessoas já é uma realidade. Dentre essas tarefas estão desde um simples acesso aos dispositivos móveis até a marcação de amigos em fotos compartilhadas em redes sociais e as complexas operações financeiras em equipamentos de autoatendimento para transações bancárias. Em 5 de julho de 2021, o governo brasileiro anunciou a compra de um sistema de reconhecimento biométrico para ser utilizado em todo território nacional. Neste sentido, este artigo propõe a abertura de uma discussão mais aprofundada sobre a adoção de tais sistemas para a identificação dos cidadãos brasileiros e quais os problemas que podem emergir se o sistema não for bem projetado, implantado e gerenciado. Além disso, uma lista de dez questões foi criada para iniciar essa conversa sobre segurança dos dados dos brasileiros na Era da Inteligência Artificial (IA) e o respeito à Lei Geral de Proteção dos Dados (LGPD).
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Phillips, P., O'Toole, A., Jiang, F., Narvekar, A., and Ayadd, J. (2009). An other-race effect for face recognition algorithms.
Ramachandra, R. and Busch, C. (2017). Presentation attack detection methods for face recognition systems: A comprehensive survey. ACM Comput. Surv., 50(1).
Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., and Cohen-Or, D. (2021). Encoding in style: A stylegan encoder for image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2287-2296.
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Seibold, C., Samek, W., Hilsmann, A., and Eisert, P. (2020). Accurate and robust neural networks for face morphing attack detection. Journal of Information Security and Applications, 53:102526.
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Yoon, S., Feng, J., and Jain, A. K. (2012). Altered fingerprints: Analysis and detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):451-464.
Zhang, H., Zhang, Z., Odena, A., and Lee, H. (2020). Consistency regularization for generative adversarial networks. In International Conference on Learning Representations - to appear.
Anjos, A., Chakka, M. M., and Marcel, S. (2014). Motion-based counter-measures to photo attacks in face recognition. IET Biom., 3:147-158.
Best-Rowden, L. and Jain, A. K. (2018). Longitudinal study of automatic face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(1):148-162.
Biometrics, I. .-. (2016). Information technology biometric presentation attack detection part 1: Framework. Technical report, International Organization for Standardization, Geneva, Switzerland.
CNIL (2019). Facial recognition: for a debate living up to the challenges. Technical report, CNIL - Commission Nationale de l'Informatique et des Libertés, Paris, France.
EUCouncil (2018). Report from the commission to the european parliament and the council on the implementation of the action plan to strengthen the eu response to travel document fraud. Technical report, Council of the European Union, Brussels, Belgium.
Faria, F. A. and Carneiro, G. (2020). Why are generative adversarial networks so fascinating and annoying? In 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 1-8.
Ferrara, M., Franco, A., and Maltoni, D. (2014). The magic passport. In IEEE International Joint Conference on Biometrics, Clearwater, IJCB 2014, FL, USA, September 29 October 2, 2014, pages 1-7. IEEE.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 27, pages 2672-2680.
Karras, T., Laine, S., and Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4401-4410.
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020). Analyzing and improving the image quality of stylegan. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8107-8116.
Mai, G., Cao, K., Yuen, P. C., and Jain, A. K. (2019). On the reconstruction of face images from deep face templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(5):1188-1202.
Ngan, M., Grother, P., Hanaoka, K., and Kuo, J. (2020). Face recognition vendor test (frvt) part 4: Morph - performance of automated face morph detection.
Park, T., Liu, M.-Y., Wang, T.-C., and Zhu, J.-Y. (2019). Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Phillips, P., O'Toole, A., Jiang, F., Narvekar, A., and Ayadd, J. (2009). An other-race effect for face recognition algorithms.
Ramachandra, R. and Busch, C. (2017). Presentation attack detection methods for face recognition systems: A comprehensive survey. ACM Comput. Surv., 50(1).
Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., and Cohen-Or, D. (2021). Encoding in style: A stylegan encoder for image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2287-2296.
Scherhag, U. J. (2020). Face Morphing and Morphing Attack detection. PhD thesis, Technische Universität Darmstadt.
Seibold, C., Samek, W., Hilsmann, A., and Eisert, P. (2020). Accurate and robust neural networks for face morphing attack detection. Journal of Information Security and Applications, 53:102526.
Venkatesh, S., Ramachandra, R., Raja, K. B., and Busch, C. (2020). Face morphing attack generation & detection: A comprehensive survey. CoRR, abs/2011.02045.
Wang, T.-C., Mallya, A., and Liu, M.-Y. (2021). One-shot free-view neural talking-head synthesis for video conferencing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10039-10049.
Yoon, S., Feng, J., and Jain, A. K. (2012). Altered fingerprints: Analysis and detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):451-464.
Zhang, H., Zhang, Z., Odena, A., and Lee, H. (2020). Consistency regularization for generative adversarial networks. In International Conference on Learning Representations - to appear.
Publicado
28/11/2022
Como Citar
TEIXEIRA, Raoni F. da S.; JANUZI, Rafael B.; FARIA, Fabio A..
The Brazilian Data at Risk in the Age of AI?. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 413-424.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2022.227520.
