# Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models

**Authors:** Peifei Zhu, Tsubasa Takahashi, Hirokatsu Kataoka  
**arXiv:** [2404.09401](https://arxiv.org/abs/2404.09401)  
**Date:** 2024-04-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.09401v1/

## Key points

- Diffusion Models (DMs) have impressive capabilities in image generation tasks
- Concerns about potential copyright infringement with DMs replicating unauthorized creations
- Proposed framework embeds personal watermarks in adversarial examples to deter DMs from mimicking unauthorized content
- Generator based on conditional adversarial networks with three key losses - adversarial loss, GAN loss, and perturbation loss
- Training the generator for a personal watermark requires only 5-10 samples in 2-3 minutes
- Generator can rapidly produce adversarial examples with specified watermark at a speed of 0.2 seconds per image
- Extensive experiments show that generated adversarial examples have visible watermarks, slightly higher image quality, and significantly faster generation times compared to existing methods
- Proposed approach incorporates visible watermarks along with chaotic textures for clarity in identifying copyright infringements
- Transferability of adversarial examples across unknown generative models shows promising results in protecting against DM-based imitation

## Summary

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In the realm of image generation tasks, Diffusion Models (DMs) have demonstrated impressive capabilities. However, there is a growing concern that these models could potentially be used to replicate unauthorized creations, leading to copyright infringement issues. To address this challenge, a novel framework has been proposed to embed personal watermarks within the generation of adversarial examples. These examples are designed to compel DMs to produce images with visible watermarks, thereby thwarting their ability to mimic unauthorized content. The proposed framework utilizes a generator based on conditional adversarial networks and incorporates three key losses - adversarial loss, GAN loss, and perturbation loss - to generate adversarial examples with subtle perturbations that effectively deter DMs from imitating copyrighted images. Notably, training the generator for a personal watermark requires only 5-10 samples within a short timeframe of 2-3 minutes. Once trained, the generator can rapidly produce adversarial examples with the specified watermark at a speed of 0.2 seconds per image. Extensive experiments have been conducted across various conditional image-generation scenarios to evaluate the efficacy of the proposed method. A comparison with existing methods reveals that the generated adversarial examples not only feature visible watermarks but also exhibit slightly higher image quality and significantly faster generation times. This advantage is particularly pronounced when generating a large number of examples. Qualitative evaluations further demonstrate that while existing methods introduce chaotic textures in generated images as a means of indicating copyright violations, the proposed approach stands out by incorporating visible watermarks in addition to chaotic textures. This straightforward visual cue enhances clarity in identifying potential copyright infringements. Moreover, the study explores the transferability of adversarial examples across unknown generative models and showcases promising results in protecting against DM-based imitation. By providing a simple yet powerful solution for safeguarding copyrights through watermark-embedded adversarial examples, this work contributes significantly towards addressing concerns related to unauthorized replication in image generation tasks.