Adversarial Examples for Preventing Diffusion Models from Malicious Image Edition

Author:

Guo MengjieORCID,Gai KekeORCID,Yu JingORCID

Publisher

Springer Nature Singapore

Reference22 articles.

1. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);B Biggio,2013

2. Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780–8794 (2021)

3. Heusel, M., Ramsauer, H., Unterthiner, T., et al.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems (NIPS), vol. 30 (2017)

4. Lei, Z., Gai, K., Yu, J., Wang, S., et al.: Efficiency-enhanced blockchain-based client selection in heterogeneous federated learning. In: 2023 IEEE International Conference on Blockchain (Blockchain), pp. 289–296. IEEE (2023)

5. Liang, C., Wu, X., Hua, Y., et al.: Adversarial example does good: preventing painting imitation from diffusion models via adversarial examples. arXiv preprint arXiv:2302.04578 (2023)

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