# Security and Privacy on Generative Data in AIGC: A Survey

arXiv: https://arxiv.org/abs/2309.09435 · HTML: https://mlsanity.com/paper/2309.09435/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2023.

## Built on by (most cited first, 30 of 34)

- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation](https://mlsanity.com/paper/2501.12612/) (2025)
- [Neural Retrievers are Biased Towards LLM-Generated Content](https://mlsanity.com/paper/2310.20501/) (2023)
- [Targeted Attack Improves Protection against Unauthorized Diffusion Customization](https://mlsanity.com/paper/2310.04687/) (2023)
- [Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models](https://mlsanity.com/paper/2408.10571/) (2024)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [Yuan: Yielding Unblemished Aesthetics Through A Unified Network for Visual Imperfections Removal in Generated Images](https://mlsanity.com/paper/2501.08505/) (2025)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge](https://mlsanity.com/paper/2508.21072/) (2025)
- [Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale](https://mlsanity.com/paper/2604.04634/) (2026)
- [U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI](https://mlsanity.com/paper/2406.15386/) (2024)
- [A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective](https://mlsanity.com/paper/2312.08248/) (2023)
- [AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2403.08542/) (2024)
- [CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion](https://mlsanity.com/paper/2403.11162/) (2024)
- [Anti-Diffusion: Preventing Abuse of Modifications of Diffusion-Based Models](https://mlsanity.com/paper/2503.05595/) (2025)
- [Protect-Your-IP: Scalable Source-Tracing and Attribution against Personalized Generation](https://mlsanity.com/paper/2405.16596/) (2024)
- [DDAP: Dual-Domain Anti-Personalization against Text-to-Image Diffusion Models](https://mlsanity.com/paper/2407.20141/) (2024)
- [Responsible Diffusion: A Comprehensive Survey on Safety, Ethics, and Trust in Diffusion Models](https://mlsanity.com/paper/2509.22723/) (2025)
- [SIGMark: Scalable In-Generation Watermark with Blind Extraction for Video Diffusion](https://mlsanity.com/paper/2603.02882/) (2026)
- [Security Risks Concerns of Generative AI in the IoT](https://mlsanity.com/paper/2404.00139/) (2024)
- [Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable](https://mlsanity.com/paper/2405.00466/) (2024)
- [Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection](https://mlsanity.com/paper/2412.06727/) (2024)
- [Detecting Malicious Concepts without Image Generation in AI-Generated Content (AIGC)](https://mlsanity.com/paper/2502.08921/) (2025)
- [AI Agents Should be Regulated Based on the Extent of Their Autonomous Operations](https://mlsanity.com/paper/2503.04750/) (2025)
- [Vision Language Model-Empowered Contract Theory for AIGC Task Allocation in Teleoperation](https://mlsanity.com/paper/2407.17428/) (2024)
- [Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential](https://mlsanity.com/paper/2501.18834/) (2025)
- [LoRAShield: Data-Free Editing Alignment for Secure Personalized LoRA Sharing](https://mlsanity.com/paper/2507.07056/) (2025)
- [MetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks](https://mlsanity.com/paper/2509.10766/) (2025)
- [RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry](https://mlsanity.com/paper/2603.01544/) (2026)

## Abstract (arXiv)

The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media forgery for fraudulent purposes. Consequently, both academia and industry begin to emphasize the trustworthiness of generative data, successively providing a series of countermeasures for security and privacy. In this survey, we systematically review the security and privacy on generative data in AIGC, particularly for the first time analyzing them from the perspective of information security properties. Specifically, we reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance, respectively. Finally, we show some representative benchmarks, present a statistical analysis, and summarize the potential exploration directions from each of theses properties.