A Truth Discovery Method with Trust-Aware Self-Supervised Model for Visual Crowdsensing

Liu Yan, Zhang Hang, Liu Pan, Yang Gang
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:84-93, 2024.

Abstract

Crowdsensing relies on truth discovery methods to obtain reliable data. However, existing truth dis-covery methods for Crowd Sensing systems face challenges in effectively evaluating the quality of uploaded image data by workers and defending against attacks by malicious workers. To overcome these issues, we introduce a truth discovery method with the trust-aware self-supervised discrim-inative (SSD) model for visual crowdsensing, namely TASSD. In the TASSD, we incorporate the worker’s trustworthiness in the reweighting mechanism of the SSD model, thereby improving system robustness and reliability. Then, we designed a trust update method to accurately obtain the worker’s trustworthiness. Experimental results demonstrate the superiority of our proposed TASSD over traditional anomaly detection methods, particularly in scenarios with high ratios of abnormal data. TASSD effectively addresses disguised malicious worker attacks that achieve high credibility.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-yan24a, title = {A Truth Discovery Method with Trust-Aware Self-Supervised Model for Visual Crowdsensing}, author = {Yan, Liu and Hang, Zhang and Pan, Liu and Gang, Yang}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {84--93}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/yan24a/yan24a.pdf}, url = {https://proceedings.mlr.press/v245/yan24a.html}, abstract = {Crowdsensing relies on truth discovery methods to obtain reliable data. However, existing truth dis-covery methods for Crowd Sensing systems face challenges in effectively evaluating the quality of uploaded image data by workers and defending against attacks by malicious workers. To overcome these issues, we introduce a truth discovery method with the trust-aware self-supervised discrim-inative (SSD) model for visual crowdsensing, namely TASSD. In the TASSD, we incorporate the worker’s trustworthiness in the reweighting mechanism of the SSD model, thereby improving system robustness and reliability. Then, we designed a trust update method to accurately obtain the worker’s trustworthiness. Experimental results demonstrate the superiority of our proposed TASSD over traditional anomaly detection methods, particularly in scenarios with high ratios of abnormal data. TASSD effectively addresses disguised malicious worker attacks that achieve high credibility. } }
Endnote
%0 Conference Paper %T A Truth Discovery Method with Trust-Aware Self-Supervised Model for Visual Crowdsensing %A Liu Yan %A Zhang Hang %A Liu Pan %A Yang Gang %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-yan24a %I PMLR %P 84--93 %U https://proceedings.mlr.press/v245/yan24a.html %V 245 %X Crowdsensing relies on truth discovery methods to obtain reliable data. However, existing truth dis-covery methods for Crowd Sensing systems face challenges in effectively evaluating the quality of uploaded image data by workers and defending against attacks by malicious workers. To overcome these issues, we introduce a truth discovery method with the trust-aware self-supervised discrim-inative (SSD) model for visual crowdsensing, namely TASSD. In the TASSD, we incorporate the worker’s trustworthiness in the reweighting mechanism of the SSD model, thereby improving system robustness and reliability. Then, we designed a trust update method to accurately obtain the worker’s trustworthiness. Experimental results demonstrate the superiority of our proposed TASSD over traditional anomaly detection methods, particularly in scenarios with high ratios of abnormal data. TASSD effectively addresses disguised malicious worker attacks that achieve high credibility.
APA
Yan, L., Hang, Z., Pan, L. & Gang, Y.. (2024). A Truth Discovery Method with Trust-Aware Self-Supervised Model for Visual Crowdsensing. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:84-93 Available from https://proceedings.mlr.press/v245/yan24a.html.

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