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Privacy-preserving vertical federated broad learning system for artificial intelligence generated image content

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Abstract

Artificial intelligence generated image content generates desired image content according to given instructions. It is widely applied in digital marketing, video game design, and movie production. How to efficiently detect the quality of the generated images is one of the key problems. A broad learning system is a feasible solution, because it requires no complex parameter calculation and depth expansion. However, the broad learning system is built by centralized learning, so it may not take advantage of isolated feature data, which prevents broad learning systems from detecting isolated AL-generated image content. To address the above problem, this paper proposes a privacy-preserving vertical federated broad learning system for artificial intelligence generated image content (PVF-BLS). In PVF-BLS, the vertical federated architecture is introduced into the broad learning system to utilize isolated feature data and interactive data are masked by matrix masks for security. To further improve the security of PVF-BLS, this paper proposes a secure incremental learning algorithm based on matrix masks (ILA-MM) to update PVF-BLS. In ILA-MM, the interaction data are processed by matrix masks, which makes PVF-BLS more secure. Experimental results show that PVF-BLS outperforms the traditional broad learning systems when the data are isolated.

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References

  1. Nikolic, P., Yang, H.: Artificial intelligence clone generated content toward robot creativity and machine mindfulness. Mob. Netw. Appl. 25, 1504–1513 (2020)

    Article  Google Scholar 

  2. Ventayen, R.J.M.: OpenAI ChatGPT generated results: similarity index of artificial intelligence-based contents. Available at SSRN 4332664 (2023)

  3. Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., Sun, L.: A comprehensive survey of ai-generated content (magic): a history of generative ai from gan to chatgpt. arXiv preprint arXiv:2303.04226 (2023)

  4. Wu, J., Gan, W., Chen, Z., Wan, S., Lin, H.: Ai-generated content (aigc): a survey. arXiv preprint arXiv:2304.06632 (2023)

  5. Gong, M., Zhao, J., Liu, J., Miao, Q., Jiao, L.: Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 27(1), 125–138 (2015)

    Article  MathSciNet  PubMed  Google Scholar 

  6. Karimian-Aliabadi, S., Ardagna, D., Entezari-Maleki, R., Gianniti, E., Movaghar, A.: Analytical composite performance models for big data applications. J. Netw. Comput. Appl. 142, 63–75 (2019)

    Article  Google Scholar 

  7. Huang, Y., Slaney, M., Seltzer, M.L., Gong, Y.: Towards better performance with heterogeneous training data in acoustic modeling using deep neural networks. In: Fifteenth Annual Conference of the International Speech Communication Association, (2014)

  8. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)

    Article  Google Scholar 

  9. Lu, D., Popuri, K., Ding, G.W., Balachandar, R., Beg, M.F.: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Sci. Rep. 8.1, 5697 (2018)

    Article  ADS  Google Scholar 

  10. Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310-1318 (2013)

  11. Chen, C.L.P., Liu, Z.: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Rrans. Neural Netw. Learn. Syst. 29.1, 10–24 (2017)

    Google Scholar 

  12. Chen, C.L.P., Wan, J.Z.: A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 29.1, 62–72 (1999)

    Article  Google Scholar 

  13. Chen, C.L.P.: A rapid supervised learning neural network for function interpolation and approximation. IEEE Trans. Neural Netw. 7.5, 1220–1230 (1996)

    Article  Google Scholar 

  14. Gong, X., et al.: Research review for broad learning system: algorithms, theory, and applications. IEEE Trans. Cybern. 52.9, 8922–8950 (2021)

    Google Scholar 

  15. Peng, C., ChunHao, D.: Monitoring multi-domain batch process state based on fuzzy broad learning system. Expert Syst. Appl. 187, 115851 (2022)

    Article  Google Scholar 

  16. Liu, Z., Huang, S., Jin, W., Mu, Y.: Graph-based broad learning system for classification. Neurocomputing 463, 535–544 (2021)

    Article  Google Scholar 

  17. Chu, F., Liang, T., Chen, C.L.P., Ma, X., Wang, X.: Broad minimax probability learning system and its application in regression modeling. IEEE Trans. Syst. Man Cybern. Syst. 53.3, 1945–1957 (2022)

    Google Scholar 

  18. Jin, J., Zhulin, L., Philip Chen, C.L.: Discriminative graph regularized broad learning system for image recognition. Sci. China Inf. Sci. 61, 1–14 (2018)

    Article  CAS  Google Scholar 

  19. Xu, L., Chen, C.L.P., Han, R.: Graph-based sparse Bayesian broad learning system for semi-supervised learning. Inf. Sci. 597, 193–210 (2022)

    Article  Google Scholar 

  20. Liu, L., Cai, L., Liu, T., Chen, C.L.P., Tang, X.: Cauchy regularized broad learning system for noisy data regression. Inf. Sci. 603, 210–221 (2022)

    Article  Google Scholar 

  21. Sheng, B., Li, P., Ali, R., Chen, C.L.P.: Improving video temporal consistency via broad learning system. IEEE Trans. Cybern. 52.7, 6662–6675 (2021)

    Google Scholar 

  22. Zhao, A., Li, J., Ahmed, M.: SpiderNet: a spiderweb graph neural network for multi-view gait recognition. Knowl.-Based Syst. 206, 106273 (2020)

    Article  Google Scholar 

  23. Zhao, A., Dong, J., Li, J., Qi, L., Zhou, H.: Associated spatio-temporal capsule network for gait recognition. IEEE Trans. Multimed. 24, 846–860 (2021)

    Article  Google Scholar 

  24. Zhao, A., Li, J., Dong, J., Qi, L., Zhang, Q., Li, N., Wang, X., Zhou, H.: Multimodal gait recognition for neurodegenerative diseases. IEEE Trans. Cybern. 52(9), 9439–9453 (2021)

    Article  Google Scholar 

  25. Zhang, Y., Yuen, K.-V.: Crack detection using fusion features-based broad learning system and image processing. Comput.-Aided Civ. Infrastr. Eng. 36(12), 1568–1584 (2021)

    Article  Google Scholar 

  26. Liu, X., Yuanqing, W.: Research on vision of intelligent car based on broad learning system. IEEE Trans. Cybern. (2022)

  27. Li, M., Andersen, D.G., Smola, A.J., Yu, K.: Communication efficient distributed machine learning with the parameter server. In: Advances in Neural Information Processing Systems, 27 (2014)

  28. Ciriani, V., De Capitani Di Vimercati, S., Foresti, S., Jajodia, S., Paraboschi, S., Samarati, P.: Combining fragmentation and encryption to protect privacy in data storage. ACM Trans. Inf. Syst. Secur. 13.3, 1–33 (2010)

    Article  Google Scholar 

  29. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)

    Article  Google Scholar 

  30. Wang, G., Dang, C.X., Zhou, Z.: Measure contribution of participants in federated learning. In: 2019 IEEE International Conference on big data (Big Data), pp. 2597–2604 (2019)

  31. Liu, Y., Kang, Y., Xing, C., Chen, T., Yang, Q.: A secure federated transfer learning framework. IEEE Intell. Syst. 35(4), 70–82 (2020)

    Article  Google Scholar 

  32. Pei, J., Zhong, K., Jan, M.A., Li, J.: Personalized federated learning framework for network traffic anomaly detection. Comput. Netw. 209, 108906 (2022)

    Article  Google Scholar 

  33. Nguyen, D.C., Pham, Q.-V., Pathirana, P.N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., Hwang, W.-J.: Federated learning for smart healthcare: aD survey. ACM Comput. Surv. (CSUR) 55.3, 1–37 (2022)

    Article  Google Scholar 

  34. Wang, Q., Yun, Z.: FedSPL: federated self-paced learning for privacy-preserving disease diagnosis. Briefings Bioinform. 23.1, bbab498 (2022)

    Article  Google Scholar 

  35. Qayyum, A., Ahmad, K., Ahsan, M.A., Al-Fuqaha, A., Qadir, J.: Collaborative federated learning for healthcare: multi-modal covid-19 diagnosis at the edge. IEEE Open J. Comput. Soc. 3, 172–184 (2022)

    Article  Google Scholar 

  36. Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Poor, H.V.: Federated learning for internet of things: a comprehensive survey. IEEE Commun. Surv. Tutor. 233, 1622–1658 (2021)

    Article  Google Scholar 

  37. Wasilewska, M., Bogucka, H., Kliks, A.: Federated learning for 5G radio spectrum sensing. Sensors 22(1), 198 (2021)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  38. Le, J., Lei, X., Mu, N., Zhang, H., Zeng, K., Liao, X.: Federated continuous learning with broad network architecture. IEEE Trans. Cybern. 51(8), 3874–3888 (2021)

    Article  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the Chunhui plan project of the Cooperative research project of the Ministry of Education of China (HZKY20220474).

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Authors and Affiliations

  1. School of Computer Science of Qufu Normal University, Rizhao, China

    Fengyin Li, Junrong Ge, Xiaojiao Wang, Xilong Yu & Xinru Li

  2. Rizhao Administrative Examination and Approval Service Bureau, Rizhao, China

    Gang Zhao

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  1. Fengyin Li
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  3. Xiaojiao Wang
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  4. Gang Zhao
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Correspondence to Fengyin Li.

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Li, F., Ge, J., Wang, X. et al. Privacy-preserving vertical federated broad learning system for artificial intelligence generated image content. J Real-Time Image Proc 21, 14 (2024). https://doi.org/10.1007/s11554-023-01393-6

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