A Machine Learning Approach for DeepFake Detection

  • Gustavo Cunha Lacerda IFB
  • Raimundo Claudio da Silva Vasconcelos IFB

Resumo


With the spread of DeepFake techniques, this technology has become quite accessible and good enough that there is concern about its malicious use. Faced with this problem, detecting forged faces is of utmost importance to ensure security and avoid socio-political problems, both on a global and private scale. This paper presents a solution for the detection of DeepFakes using convolution neural networks and a dataset developed for this purpose - Celeb-DF. The results show that, with an overall accuracy of 95% in the classification of these images, the proposed model is close to what exists in the state of the art with the possibility of adjustment for better results in the manipulation techniques that arise in the future.

Referências

Q. Xul, X. Zou, and J. Zhao, "On-line detection of defects on fruit by machinevision systems based on three-color-cameras systems," in International Conference on Computer and Computing Technologies in Agriculture. Springer, 2008, pp. 2231-2238.

H. R. Carolyn Giarda. (2022) How "˜furious 7' brought the late paul walker back to life. [Online]. Available: [link].

T. N. T. Helen Rosner. (2022) The ethics of a deepfake anthony bourdain voice. [Online]. Available: [link].

B. Dolhansky, J. Bitton, B. Pflaum, J. Lu, R. Howes, M. Wang, and C. C. Ferrer, "The deepfake detection challenge (dfdc) dataset," arXiv preprint arXiv:2006.07397, 2020.

D. . T. most advanced scientific research database. (2022) MS Windows NT kernel description. [Online]. Available: https://www.dimensions.ai/

Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, "Celeb-df (v2): a new dataset for deepfake forensics," arXiv preprint arXiv:1909.12962, 2019.

D. Güera and E. J. Delp, "Deepfake video detection using recurrent neural networks," in 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE, 2018, pp. 1-6.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818-2826.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009, pp. 248-255.

O. de Lima, S. Franklin, S. Basu, B. Karwoski, and A. George, "Deepfake detection using spatiotemporal convolutional networks," arXiv preprint arXiv:2006.14749, 2020.

L. Guarnera, O. Giudice, and S. Battiato, "Deepfake detection by analyzing convolutional traces," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2020, pp. 666-667.

C. Lugaresi, J. Tang, H. Nash, C. McClanahan, E. Uboweja, M. Hays, F. Zhang, C.-L. Chang, M. G. Yong, J. Lee et al., "Mediapipe: A framework for building perception pipelines," arXiv preprint arXiv:1906.08172, 2019.

D. S. A. Team. (2022) Capítulo 40 - introdução as redes neurais convolucionais. [Online]. Available: [link].

B. Koonce, "Efficientnet," in Convolutional neural networks with swift for tensorflow. Springer, 2021, pp. 109-123.

A. Balaji, Y. Wu, and J. Yoon, "Cifar100 convolutional model based classification benchmark."

M.-E. Nilsback and A. Zisserman, "Automated flower classification over a large number of classes," in 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing. IEEE, 2008, pp. 722-729.
Publicado
24/10/2022
LACERDA, Gustavo Cunha; VASCONCELOS, Raimundo Claudio da Silva. A Machine Learning Approach for DeepFake Detection. In: WORKSHOP DE TRABALHOS DA GRADUAÇÃO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 35. , 2022, Natal/RN. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 110-113. DOI: https://doi.org/10.5753/sibgrapi.est.2022.23272.