Abstract
Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style. To address these emerging copyright violations, in this paper, we are the first to explore and propose to utilize adversarial examples for DMs to protect human-created artworks. Specifically, we first build a theoretical framework to define and evaluate the adversarial examples for DMs. Then, based on this framework, we design a novel algorithm, named AdvDM, which exploits a Monte-Carlo estimation of adversarial examples for DMs by optimizing upon different latent variables sampled from the reverse process of DMs. Extensive experiments show that the generated adversarial examples can effectively hinder DMs from extracting their features. Therefore, our method can be a powerful tool for human artists to protect their copyright against infringers equipped with DM-based AI-for-Art applications. The code of our method is available on GitHub: https://github.com/mist-project/mist.git.
Original language | English |
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Title of host publication | Proceedings of the 40th International Conference on Machine Learning |
Editors | Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett |
Pages | 20763-20786 |
Number of pages | 24 |
Volume | 202 |
Publication status | Published - 31 Jul 2023 |
Event | 40th International Conference on Machine Learning, ICML 2023 - Honolulu, United States Duration: 23 Jul 2023 → 29 Jul 2023 |
Publication series
Name | Proceedings of Machine Learning Research |
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ISSN (Electronic) | 2640-3498 |
Conference
Conference | 40th International Conference on Machine Learning, ICML 2023 |
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Country/Territory | United States |
City | Honolulu |
Period | 23/07/2023 → 29/07/2023 |
Bibliographical note
Publisher Copyright:© 2023 Proceedings of Machine Learning Research. All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability