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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

Thu Jul 22 09:00 PM -- 11:00 PM (PDT) @

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.

Author Information

Jiaxiang Ren (Auburn University)
Zijie Zhang (Auburn University)
Jiayin Jin (Auburn University)
Xin Zhao (Auburn University)
Sixing Wu (Peking University)
Yang Zhou (Auburn University)
Yelong Shen (Microsoft Dynamics 365 AI)
Tianshi Che (Auburn University)
Ruoming Jin (Kent State University)
Dejing Dou (" University of Oregon, USA")

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