Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness

TL;DR

TrustGAIN addresses the trustworthiness challenges of AI-generated content in future networks by unifying robustness against adversarial inputs, strict data security, and fairness across heterogeneous user groups. Key contributions include a framework integrating data sanitization, adversarial training, and model verification for robustness; security measures like data protection, access control, and privacy-preserving processing; and a three-phase fairness pipeline (pre-, in-, post-processing) to reduce bias. A case study introduces SAD, a sentiment-analysis-based detector to identify LLM-generated unsafe content, showing superior performance on fake-news, code, and review datasets. Results demonstrate that TrustGAIN can support secure, reliable, and equitable AIGC services in edge, metaverse, and IoT-enabled networks, highlighting its potential for practical deployment.

Abstract

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.

Figures (5)

• Figure 1: Application scenarios of trustworthy AIGC-enhanced intelligent network.
• Figure 2: The architecture of TrustGAIN, the novel paradigm of trustworthy AIGC for future networks.
• Figure 3: The robustness of AIGC models for intelligent network services, including model-centric attack, poisoning attack, and prompt injection.
• Figure 4: Trustworthy AIGC for achieving more fair network services. The specific methods are divided into three phases: pre-processing, in-processing, and post-processing.
• Figure 5: The detection performance of GPT-generated fake news under different input lengths.