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Recently, many studies utilized adversarial examples (AEs) to raise the cost of malicious image editing and copyright violation powered by latent diffusion models (LDMs).
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
LSUN: construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
Earlier work this paper cites.
Improved techniques for training gans
Salimans, T., Goodfellow, I. J., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Earlier work this paper cites.
Countering adversarial images using input transformations
Guo, C., Rana, M., Cissé, M., and van der Maaten, L · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Earlier work this paper cites.
Learning transferable adversarial examples via ghost networks
Li, Y., Bai, S., Zhou, Y., Xie, C., Zhang, Z., and Yuille, A. L · 2020
Earlier work this paper cites.
Contrastive language-image pre-training for the italian language
Bianchi, F., Attanasio, G., Pisoni, R., Terragni, S., Sarti, G., and Lakshmi, S · 2021
Earlier work this paper cites.
Adversarial examples make strong poisons
Fowl, L., Goldblum, M., Chiang, P., Geiping, J., Czaja, W., and Goldstein, T · 2021
Earlier work this paper cites.
Unlearnable examples: Making personal data unexploitable
Huang, H., Ma, X., Erfani, S. M., Bailey, J., and Wang, Y · 2021
Cited alongside, same era.
A little robustness goes a long way: Leveraging robust features for targeted transfer attacks
Springer, J. M., Mitchell, M., and Kenyon, G. T · 2021
Cited alongside, same era.
TRS: transferability reduced ensemble via promoting gradient diversity and model smoothness
Yang, Z., Li, L., Xu, X., Zuo, S., Chen, Q., Zhou, P., Rubinstein, B. I. P., Zhang, C., and Li, B · 2021
Cited alongside, same era.
On success and simplicity: A second look at transferable targeted attacks
Zhao, Z., Liu, Z., and Larson, M. A · 2021
Cited alongside, same era.
LGV: boosting adversarial example transferability from large geometric vicinity
Gubri, M., Cordy, M., Papadakis, M., Traon, Y. L., and Sen, K · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
An image is worth one word: Personalizing text-to-image generation using textual inversion
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., and Cohen-Or, D · 2023
Later among the works it cites.
Anti-dreambooth: Protecting users from personalized text-to-image synthesis
Le, T. V., Phung, H., Nguyen, T. H., Dao, Q., Tran, N., and Tran, A · 2023
Later among the works it cites.
Mist: Towards improved adversarial examples for diffusion models
Liang, C. and Wu, X · 2023
Later among the works it cites.
Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples
Liang, C., Wu, X., Hua, Y., Zhang, J., Xue, Y., Song, T., Xue, Z., Ma, R., and Guan, H · 2023
Later among the works it cites.
Raising the cost of malicious ai-powered image editing
Salman, H., Khaddaj, A., Leclerc, G., Ilyas, A., and Madry, A · 2023
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Diffusers: State-of-the-art diffusion models
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., and Wolf, T · 2022
Cited alongside, same era.
Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability
Xiong, Y., Lin, J., Zhang, M., Hopcroft, J. E., and He, K · 2022
Cited alongside, same era.
An adaptive model ensemble adversarial attack for boosting adversarial transferability
Chen, B., Yin, J., Chen, S., Chen, B., and Liu, X · 2023
Cited alongside, same era.
On the robustness of latent diffusion models
Zhang, J., Xu, Z., Cui, S., Meng, C., Wu, W., and Lyu, M. R
Cited in the paper.
Why does little robustness help? a further step towards understanding adversarial transferability, 07 2023b
Zhang, Y., Hu, S., Zhang, L., Shi, J., Li, M., Liu, X., Wan, W., and Jin, H
Cited in the paper.
Later among the works it cites.
JPEG compressed images can bypass protections against AI editing
Segura, P. S., Geiping, J., and Goldstein, T · 2023
Later among the works it cites.
SimAC: A Simple Anti-Customization Method against Text-to-Image Synthesis of Diffusion Models
Wang, F., Tan, Z., Wei, T., Wu, Y., and Huang, Q · 2023
Later among the works it cites.
Exploring CLIP for assessing the look and feel of images
Wang, J., Chan, K. C. K., and Loy, C. C · 2023
Later among the works it cites.
Toward effective protection against diffusion based mimicry through score distillation
Xue, H., Liang, C., Wu, X., and Chen, Y · 2023
Later among the works it cites.