Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909106191728640 |
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| author | Yang, Shiyi Yao, Lina Wang, Chen Xu, Xiwei Zhu, Liming |
| author_facet | Yang, Shiyi Yao, Lina Wang, Chen Xu, Xiwei Zhu, Liming |
| contents | Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods that use limited resources to generate high-quality fake user profiles to achieve 1) transferability among black-box RSs 2) and imperceptibility among detectors. In order to achieve these goals, we introduce textual reviews of products to enhance the generation quality of the profiles. Specifically, we propose a novel attack framework named R-Trojan, which formulates the attack objectives as an optimization problem and adopts a tailored transformer-based generative adversarial network (GAN) to solve it so that high-quality attack profiles can be produced. Comprehensive experiments on real-world datasets demonstrate that R-Trojan greatly outperforms state-of-the-art attack methods on various victim RSs under black-box settings and show its good imperceptibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09023 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems Yang, Shiyi Yao, Lina Wang, Chen Xu, Xiwei Zhu, Liming Cryptography and Security Artificial Intelligence Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods that use limited resources to generate high-quality fake user profiles to achieve 1) transferability among black-box RSs 2) and imperceptibility among detectors. In order to achieve these goals, we introduce textual reviews of products to enhance the generation quality of the profiles. Specifically, we propose a novel attack framework named R-Trojan, which formulates the attack objectives as an optimization problem and adopts a tailored transformer-based generative adversarial network (GAN) to solve it so that high-quality attack profiles can be produced. Comprehensive experiments on real-world datasets demonstrate that R-Trojan greatly outperforms state-of-the-art attack methods on various victim RSs under black-box settings and show its good imperceptibility. |
| title | Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2402.09023 |