# Copyright Protection and Accountability of Generative AI:Attack, Watermarking and Attribution

arXiv: https://arxiv.org/abs/2303.09272 · HTML: https://mlsanity.com/paper/2303.09272/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 17 of its 20 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 17 of 17)

- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data](https://mlsanity.com/paper/2007.08457/) (2020)
- [StyleSwin: Transformer-based GAN for High-resolution Image Generation](https://mlsanity.com/paper/2112.10762/) (2021)
- [Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems](https://mlsanity.com/paper/2003.01279/) (2020)
- [CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes](https://mlsanity.com/paper/2105.10872/) (2021)
- [Deepfake Network Architecture Attribution](https://mlsanity.com/paper/2202.13843/) (2022)
- [Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack](https://mlsanity.com/paper/2102.04362/) (2021)
- [Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models](https://mlsanity.com/paper/2112.05588/) (2021)
- [Supervised GAN Watermarking for Intellectual Property Protection](https://mlsanity.com/paper/2209.03466/) (2022)

## Built on by (most cited first, 15 of 15)

- [Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey](https://mlsanity.com/paper/2310.15264/) (2023)
- [Automatic Jailbreaking of the Text-to-Image Generative AI Systems](https://mlsanity.com/paper/2405.16567/) (2024)
- [GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis](https://mlsanity.com/paper/2407.10471/) (2024)
- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [Defenses in Adversarial Machine Learning: A Survey](https://mlsanity.com/paper/2312.08890/) (2023)
- [U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI](https://mlsanity.com/paper/2406.15386/) (2024)
- [Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models](https://mlsanity.com/paper/2503.09805/) (2025)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [The Theater Stage as Laboratory: Review of Real-Time Comedy LLM Systems for Live Performance](https://mlsanity.com/paper/2501.08474/) (2025)
- [AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models](https://mlsanity.com/paper/2509.00641/) (2025)
- [Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI](https://mlsanity.com/paper/2404.08221/) (2024)
- [Psittacines of Innovation? Assessing the True Novelty of AI Creations](https://mlsanity.com/paper/2404.00017/) (2024)
- [Open Set Face Forgery Detection via Dual-Level Evidence Collection](https://mlsanity.com/paper/2512.04331/) (2025)
- [Semantic Differentiation for Tackling Challenges in Watermarking Low-Entropy Constrained Generation Outputs](https://mlsanity.com/paper/2601.11629/) (2026)

## Abstract (arXiv)

Generative AI (e.g., Generative Adversarial Networks - GANs) has become increasingly popular in recent years. However, Generative AI introduces significant concerns regarding the protection of Intellectual Property Rights (IPR) (resp. model accountability) pertaining to images (resp. toxic images) and models (resp. poisoned models) generated. In this paper, we propose an evaluation framework to provide a comprehensive overview of the current state of the copyright protection measures for GANs, evaluate their performance across a diverse range of GAN architectures, and identify the factors that affect their performance and future research directions. Our findings indicate that the current IPR protection methods for input images, model watermarking, and attribution networks are largely satisfactory for a wide range of GANs. We highlight that further attention must be directed towards protecting training sets, as the current approaches fail to provide robust IPR protection and provenance tracing on training sets.