Integrated Defense for Resilient Graph Matching

Jiaxiang Ren, Zijie Zhang, Jiayin Jin, Xin Zhao, Sixing Wu, Yang Zhou, Yelong Shen, Tianshi Che, Ruoming Jin, Dejing Dou
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8982-8997, 2021.

Abstract

A recent study has shown that graph matching models are vulnerable to adversarial manipulation of their input which is intended to cause a mismatching. Nevertheless, there is still a lack of a comprehensive solution for further enhancing the robustness of graph matching against adversarial attacks. In this paper, we identify and study two types of unique topology attacks in graph matching: inter-graph dispersion and intra-graph assembly attacks. We propose an integrated defense model, IDRGM, for resilient graph matching with two novel defense techniques to defend against the above two attacks simultaneously. A detection technique of inscribed simplexes in the hyperspheres consisting of multiple matched nodes is proposed to tackle inter-graph dispersion attacks, in which the distances among the matched nodes in multiple graphs are maximized to form regular simplexes. A node separation method based on phase-type distribution and maximum likelihood estimation is developed to estimate the distribution of perturbed graphs and separate the nodes within the same graphs over a wide space, for defending intra-graph assembly attacks, such that the interference from the similar neighbors of the perturbed nodes is significantly reduced. We evaluate the robustness of our IDRGM model on real datasets against state-of-the-art algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-ren21c, title = {Integrated Defense for Resilient Graph Matching}, author = {Ren, Jiaxiang and Zhang, Zijie and Jin, Jiayin and Zhao, Xin and Wu, Sixing and Zhou, Yang and Shen, Yelong and Che, Tianshi and Jin, Ruoming and Dou, Dejing}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {8982--8997}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/ren21c/ren21c.pdf}, url = {https://proceedings.mlr.press/v139/ren21c.html}, abstract = {A recent study has shown that graph matching models are vulnerable to adversarial manipulation of their input which is intended to cause a mismatching. Nevertheless, there is still a lack of a comprehensive solution for further enhancing the robustness of graph matching against adversarial attacks. In this paper, we identify and study two types of unique topology attacks in graph matching: inter-graph dispersion and intra-graph assembly attacks. We propose an integrated defense model, IDRGM, for resilient graph matching with two novel defense techniques to defend against the above two attacks simultaneously. A detection technique of inscribed simplexes in the hyperspheres consisting of multiple matched nodes is proposed to tackle inter-graph dispersion attacks, in which the distances among the matched nodes in multiple graphs are maximized to form regular simplexes. A node separation method based on phase-type distribution and maximum likelihood estimation is developed to estimate the distribution of perturbed graphs and separate the nodes within the same graphs over a wide space, for defending intra-graph assembly attacks, such that the interference from the similar neighbors of the perturbed nodes is significantly reduced. We evaluate the robustness of our IDRGM model on real datasets against state-of-the-art algorithms.} }
Endnote
%0 Conference Paper %T Integrated Defense for Resilient Graph Matching %A Jiaxiang Ren %A Zijie Zhang %A Jiayin Jin %A Xin Zhao %A Sixing Wu %A Yang Zhou %A Yelong Shen %A Tianshi Che %A Ruoming Jin %A Dejing Dou %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-ren21c %I PMLR %P 8982--8997 %U https://proceedings.mlr.press/v139/ren21c.html %V 139 %X A recent study has shown that graph matching models are vulnerable to adversarial manipulation of their input which is intended to cause a mismatching. Nevertheless, there is still a lack of a comprehensive solution for further enhancing the robustness of graph matching against adversarial attacks. In this paper, we identify and study two types of unique topology attacks in graph matching: inter-graph dispersion and intra-graph assembly attacks. We propose an integrated defense model, IDRGM, for resilient graph matching with two novel defense techniques to defend against the above two attacks simultaneously. A detection technique of inscribed simplexes in the hyperspheres consisting of multiple matched nodes is proposed to tackle inter-graph dispersion attacks, in which the distances among the matched nodes in multiple graphs are maximized to form regular simplexes. A node separation method based on phase-type distribution and maximum likelihood estimation is developed to estimate the distribution of perturbed graphs and separate the nodes within the same graphs over a wide space, for defending intra-graph assembly attacks, such that the interference from the similar neighbors of the perturbed nodes is significantly reduced. We evaluate the robustness of our IDRGM model on real datasets against state-of-the-art algorithms.
APA
Ren, J., Zhang, Z., Jin, J., Zhao, X., Wu, S., Zhou, Y., Shen, Y., Che, T., Jin, R. & Dou, D.. (2021). Integrated Defense for Resilient Graph Matching. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:8982-8997 Available from https://proceedings.mlr.press/v139/ren21c.html.

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