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Application of Convolution BLS in AI Face-Changing Problem

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Cognitive Systems and Signal Processing (ICCSIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1397))

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Abstract

AI face-changing is a new technology that is developing in recent years, and its appearance may bring unnecessary trouble to certain fields of human society. On special occasions, there is an urgent need for a model that can autonomously determine whether an image has undergone AI face-exchanging processing.Proposed two convolutional structures based on the braod learning system (BLS), and give the specific algorithm flow. Using the convolution method for the basic BLS structure, try to directly convolve the feature mapping layer and the input data. The experimental results show that the BLS with convolutional structure performs well on small face data sets, and a very simple structure can achieve satisfactory accuracy and training time on the data set. It reflects the simplicity and reliability of the BLS-Convolution structure.

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References

  1. Feng, S., Shen, C., Xia, N., Song, W., Fan, M., Cowling, B.J.: Rational use of face masks in the COVID-19 pandemic. LANCET-2600(20)30134-X (2020)

    Google Scholar 

  2. Sun, Y., Liang, D., Wang, X., Tang, X.: Face recognition with very deep neural networks. arXiv:1502.00873 [cs.CV] (2015)

  3. Grm, K., Struc, V., Artiges, A., Caron, M., Ekenel, H.K.: Strengths and weaknesses of deep learning models for face recognition against image degradations, vol. 7, no. 1, pp. 81–89, January 2018

    Google Scholar 

  4. Song, Q., Ni, J., Wang, G.: A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans. Knowl. Data Eng. 25(1), 1–14 (2013)

    Article  Google Scholar 

  5. Esmin, A., Coelho, R., Matwin, S.: A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif. Intell. Rev. 44(1), 23–45 (2013). https://doi.org/10.1007/s10462-013-9400-4

    Article  Google Scholar 

  6. Aggarwal, C.C., Yu, P.S.: An effective and efficient algorithm for high-dimensional outlier detection. VLDB J. 14, 211–221 (2005). https://doi.org/10.1007/s00778-004-0125-5

    Article  Google Scholar 

  7. Chen, C.L.P., Liu, Z.: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans. Neural Netw. Learn. Syst. 29(1), 10–24 (2018)

    Article  MathSciNet  Google Scholar 

  8. Wang, W., Xiao, Q., Bao, C., Yu, Z.: GA-BLS-GNN: a broad extension of graph neural network. Supported by the National Natural Science Foundation of China (41571299)

    Google Scholar 

  9. Greville, T.N.E.: Some Applications of the Pseudoinverse of a Matrix. SLAM Rev. 2(1), 15–22 (1960)

    MathSciNet  MATH  Google Scholar 

  10. Marquardt, D., Snee, R.: Ridge regression in practice. Am. Stat. 29(1), 3–20 (1975). https://doi.org/10.1080/00031305.1975.10479105

    Article  MATH  Google Scholar 

  11. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Association, pp. 1–12 (2015)

    Google Scholar 

  12. Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: FaceForensics: a large-scale video dataset for forgery detection in human faces. arXiv:1803.09179 [cs.CV] (2018)

  13. Ma, J., Yu, M., Fong, S., et al.: Using deep learning to model the hierarchical structure and function of a cell. Nat. Methods 15, 290–298 (2018)

    Article  Google Scholar 

  14. Jia, S., Lansdall-Welfare, T., Cristianini, N.: Gender classification by deep learning on millions of weakly labelled images. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, pp. 462–467 (2016). https://doi.org/10.1109/ICDMW.2016.0072

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Correspondence to Wenfeng Wang .

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Wang, J., Li, X., Wang, W., Huang, J. (2021). Application of Convolution BLS in AI Face-Changing Problem. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_15

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  • DOI: https://doi.org/10.1007/978-981-16-2336-3_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2335-6

  • Online ISBN: 978-981-16-2336-3

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