# Generative Model Watermarking Based on Human Visual System

arXiv: https://arxiv.org/abs/2209.15268 · HTML: https://mlsanity.com/paper/2209.15268/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [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)
- [Model Watermarking for Image Processing Networks](https://mlsanity.com/paper/2002.11088/) (2020)
- [Spread-Transform Dither Modulation Watermarking of Deep Neural Network](https://mlsanity.com/paper/2012.14171/) (2020)
- [Watermarking Graph Neural Networks by Random Graphs](https://mlsanity.com/paper/2011.00512/) (2020)
- [Structural Watermarking to Deep Neural Networks via Network Channel Pruning](https://mlsanity.com/paper/2107.08688/) (2021)
- [Verifying Integrity of Deep Ensemble Models by Lossless Black-box Watermarking with Sensitive Samples](https://mlsanity.com/paper/2205.04145/) (2022)
- [Hiding Data Hiding](https://mlsanity.com/paper/2102.06826/) (2021)
- [Robust and Lossless Fingerprinting of Deep Neural Networks via Pooled Membership Inference](https://mlsanity.com/paper/2209.04113/) (2022)

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

- [Copyright Protection in Generative AI: A Technical Perspective](https://mlsanity.com/paper/2402.02333/) (2024)
- [Generative Model Watermarking Suppressing High-Frequency Artifacts](https://mlsanity.com/paper/2305.12391/) (2023)
- [Yet Another Watermark for Large Language Models](https://mlsanity.com/paper/2509.12574/) (2025)

## Abstract (arXiv)

Intellectual property protection of deep neural networks is receiving attention from more and more researchers, and the latest research applies model watermarking to generative models for image processing. However, the existing watermarking methods designed for generative models do not take into account the effects of different channels of sample images on watermarking. As a result, the watermarking performance is still limited. To tackle this problem, in this paper, we first analyze the effects of embedding watermark information on different channels. Then, based on the characteristics of human visual system (HVS), we introduce two HVS-based generative model watermarking methods, which are realized in RGB color space and YUV color space respectively. In RGB color space, the watermark is embedded into the R and B channels based on the fact that HVS is more sensitive to G channel. In YUV color space, the watermark is embedded into the DCT domain of U and V channels based on the fact that HVS is more sensitive to brightness changes. Experimental results demonstrate the effectiveness of the proposed work, which improves the fidelity of the model to be protected and has good universality compared with previous methods.