# Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack

arXiv: https://arxiv.org/abs/2102.04362 · HTML: https://mlsanity.com/paper/2102.04362/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Adversarial Frontier Stitching for Remote Neural Network Watermarking](https://mlsanity.com/paper/1711.01894/) (2017)
- [[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks](https://mlsanity.com/paper/1909.07830/) (2019)
- [Passport-aware Normalization for Deep Model Protection](https://mlsanity.com/paper/2010.15824/) (2020)
- [DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks](https://mlsanity.com/paper/1804.03648/) (2018)
- [DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models](https://mlsanity.com/paper/1804.00750/) (2018)

## Built on by (most cited first, 30 of 46)

- [A Recipe for Watermarking Diffusion Models](https://mlsanity.com/paper/2303.10137/) (2023)
- [Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning](https://mlsanity.com/paper/2305.04175/) (2023)
- [Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations](https://mlsanity.com/paper/2202.08602/) (2022)
- [WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2306.04744/) (2023)
- [Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.10701/) (2023)
- [Supervised GAN Watermarking for Intellectual Property Protection](https://mlsanity.com/paper/2209.03466/) (2022)
- [Copyright Protection in Generative AI: A Technical Perspective](https://mlsanity.com/paper/2402.02333/) (2024)
- [PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators](https://mlsanity.com/paper/2304.07361/) (2023)
- [A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions](https://mlsanity.com/paper/2305.18339/) (2023)
- [FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model](https://mlsanity.com/paper/2211.07160/) (2022)
- [SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models](https://mlsanity.com/paper/2412.04852/) (2024)
- [Watermarking Techniques for Large Language Models: A Survey](https://mlsanity.com/paper/2409.00089/) (2024)
- [GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis](https://mlsanity.com/paper/2407.10471/) (2024)
- [Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation](https://mlsanity.com/paper/2303.09732/) (2023)
- [Intellectual Property Protection of Diffusion Models via the Watermark Diffusion Process](https://mlsanity.com/paper/2306.03436/) (2023)
- [Copyright Protection and Accountability of Generative AI:Attack, Watermarking and Attribution](https://mlsanity.com/paper/2303.09272/) (2023)
- [Functional Invariants to Watermark Large Transformers](https://mlsanity.com/paper/2310.11446/) (2023)
- [On the Exploitation of Deepfake Model Recognition](https://mlsanity.com/paper/2204.04513/) (2022)
- [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)
- [Safe-SD: Safe and Traceable Stable Diffusion with Text Prompt Trigger for Invisible Generative Watermarking](https://mlsanity.com/paper/2407.13188/) (2024)
- [Single-Model Attribution of Generative Models Through Final-Layer Inversion](https://mlsanity.com/paper/2306.06210/) (2023)
- [What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network](https://mlsanity.com/paper/2307.15860/) (2023)
- [Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs](https://mlsanity.com/paper/2310.16919/) (2023)
- [Invisible Watermarking for Audio Generation Diffusion Models](https://mlsanity.com/paper/2309.13166/) (2023)
- [Cracking White-box DNN Watermarks via Invariant Neuron Transforms](https://mlsanity.com/paper/2205.00199/) (2022)
- [Robust Retraining-free GAN Fingerprinting via Personalized Normalization](https://mlsanity.com/paper/2311.05478/) (2023)
- [Robust and Large-Payload DNN Watermarking via Fixed, Distribution-Optimized, Weights](https://mlsanity.com/paper/2208.10973/) (2022)
- [T2IW: Joint Text to Image & Watermark Generation](https://mlsanity.com/paper/2309.03815/) (2023)
- [Steganographic Passport: An Owner and User Verifiable Credential for Deep Model IP Protection Without Retraining](https://mlsanity.com/paper/2404.02889/) (2024)
- [AEDR: Training-Free AI-Generated Image Attribution via Autoencoder Double-Reconstruction](https://mlsanity.com/paper/2507.18988/) (2025)

## Abstract (arXiv)

Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. To the best of our knowledge, one of the prominent deep learning models - Generative Adversarial Networks (GANs) which has been widely used to create photorealistic image are totally unprotected despite the existence of pioneering IPR protection methodology for Convolutional Neural Networks (CNNs). This paper therefore presents a complete protection framework in both black-box and white-box settings to enforce IPR protection on GANs. Empirically, we show that the proposed method does not compromise the original GANs performance (i.e. image generation, image super-resolution, style transfer), and at the same time, it is able to withstand both removal and ambiguity attacks against embedded watermarks.