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

arXiv: https://arxiv.org/abs/2405.05930 · HTML: https://mlsanity.com/paper/2405.05930/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 12 of 12)

- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Analyzing Leakage of Personally Identifiable Information in Language Models](https://mlsanity.com/paper/2302.00539/) (2023)
- [Poisoning Language Models During Instruction Tuning](https://mlsanity.com/paper/2305.00944/) (2023)
- [Sponge Examples: Energy-Latency Attacks on Neural Networks](https://mlsanity.com/paper/2006.03463/) (2020)
- [Bias of AI-Generated Content: An Examination of News Produced by Large Language Models](https://mlsanity.com/paper/2309.09825/) (2023)
- [Fair Generative Models via Transfer Learning](https://mlsanity.com/paper/2212.00926/) (2022)
- [Improving Fairness using Vision-Language Driven Image Augmentation](https://mlsanity.com/paper/2311.01573/) (2023)
- [A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks](https://mlsanity.com/paper/2309.01426/) (2023)
- [Threat Assessment in Machine Learning based Systems](https://mlsanity.com/paper/2207.00091/) (2022)
- [Spear or Shield: Leveraging Generative AI to Tackle Security Threats of Intelligent Network Services](https://mlsanity.com/paper/2306.02384/) (2023)
- [Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks](https://mlsanity.com/paper/2405.02972/) (2024)

## Built on by (most cited first, 3 of 3)

- [HoneyTrap: Deceiving Large Language Model Attackers to Honeypot Traps with Resilient Multi-Agent Defense](https://mlsanity.com/paper/2601.04034/) (2026)
- [DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis](https://mlsanity.com/paper/2604.26328/) (2026)
- [When Agentic Trust Crosses Organizational Boundaries: Structural Externalization and a Reference Model for Trust Evidence](https://mlsanity.com/paper/2609.22961/) (2026)

## Abstract (arXiv)

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.