The adversarial robustness of recommendation systems under node injection attacks has received considerable research attention. Recently, a robust recommendation system GraphRfi was proposed, and it was shown that GraphRfi could successfully mitigate the effects of injected fake users in the system. Unfortunately, we demonstrate that GraphRfi is still vulnerable to attacks due to the supervised nature of its fraudster detection component. Specifically, we propose a new attack metaC against GraphRfi, and further analyze why GraphRfi fails under such an attack. Based on the insights we obtained from the vulnerability analysis, we build a new robust recommendation system PDR by re-designing the fraudster detection component. Comprehensive experiments show that our defense approach outperforms other benchmark methods under attacks. Overall, our research demonstrates an effective framework of integrating fraudster detection into recommendation to achieve adversarial robustness.

翻译：在节点注射攻击下,建议系统的对抗性强力得到了相当的研究关注。最近,提出了强有力的建议系统GapraRfi,并表明GapraRfi能够成功地减轻系统中注射假用户的影响。不幸的是,我们证明GapraRfi由于其欺诈者侦查部分的监督性质,仍然容易受到攻击。具体地说,我们提议对GapaRfi进行新的攻击元C,并进一步分析为什么在这种攻击下,GapraRfi不能成功。根据我们从脆弱性分析中获得的洞察,我们通过重新设计欺诈者侦查部分,建立了一个新的强有力的建议系统PDR。全面实验表明,我们的防御方法比其他受攻击的基准方法要好。总体而言,我们的研究表明,将欺诈者侦查纳入建议以达到对抗性强力的有效框架。