Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929733091983360 |
|---|---|
| author | Li, Siyuan Lin, Xi Liu, Yaju Chen, Xiang Li, Jianhua |
| author_facet | Li, Siyuan Lin, Xi Liu, Yaju Chen, Xiang Li, Jianhua |
| contents | AI-generated content (AIGC) models, represented by large language models (LLM), have revolutionized content creation. High-speed next-generation communication technology is an ideal platform for providing powerful AIGC network services. At the same time, advanced AIGC techniques can also make future network services more intelligent, especially various online content generation services. However, the significant untrustworthiness concerns of current AIGC models, such as robustness, security, and fairness, greatly affect the credibility of intelligent network services, especially in ensuring secure AIGC services. This paper proposes TrustGAIN, a trustworthy AIGC framework that incorporates robust, secure, and fair network services. We first discuss the robustness to adversarial attacks faced by AIGC models in network systems and the corresponding protection issues. Subsequently, we emphasize the importance of avoiding unsafe and illegal services and ensuring the fairness of the AIGC network services. Then as a case study, we propose a novel sentiment analysis-based detection method to guide the robust detection of unsafe content in network services. We conduct our experiments on fake news, malicious code, and unsafe review datasets to represent LLM application scenarios. Our results indicate that TrustGAIN is an exploration of future networks that can support trustworthy AIGC network services. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05930 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness Li, Siyuan Lin, Xi Liu, Yaju Chen, Xiang Li, Jianhua Cryptography and Security Artificial Intelligence Networking and Internet Architecture AI-generated content (AIGC) models, represented by large language models (LLM), have revolutionized content creation. High-speed next-generation communication technology is an ideal platform for providing powerful AIGC network services. At the same time, advanced AIGC techniques can also make future network services more intelligent, especially various online content generation services. However, the significant untrustworthiness concerns of current AIGC models, such as robustness, security, and fairness, greatly affect the credibility of intelligent network services, especially in ensuring secure AIGC services. This paper proposes TrustGAIN, a trustworthy AIGC framework that incorporates robust, secure, and fair network services. We first discuss the robustness to adversarial attacks faced by AIGC models in network systems and the corresponding protection issues. Subsequently, we emphasize the importance of avoiding unsafe and illegal services and ensuring the fairness of the AIGC network services. Then as a case study, we propose a novel sentiment analysis-based detection method to guide the robust detection of unsafe content in network services. We conduct our experiments on fake news, malicious code, and unsafe review datasets to represent LLM application scenarios. Our results indicate that TrustGAIN is an exploration of future networks that can support trustworthy AIGC network services. |
| title | Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness |
| topic | Cryptography and Security Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2405.05930 |