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Latent generative models (e.g., Stable Diffusion) have become more and more popular, but concerns have arisen regarding potential misuse related to images generated by these models.
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Wang, S.-Y., Wang, O., Zhang, R., Owens, A., and Efros, A. A · 2020
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Disrupting and preventing deepfake abuse: Exploring criminal law responses to ai-facilitated abuse
Flynn, A., Clough, J., and Cooke, T · 2021
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Fixed neural network steganography: Train the images, not the network
Kishore, V., Chen, X., Wang, Y., Li, B., and Weinberger, K. Q · 2021
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The creation and detection of deepfakes: A survey
Mirsky, Y. and Lee, W · 2021
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The stable signature: Rooting watermarks in latent diffusion models
Fernandez, P., Couairon, G., Jégou, H., Douze, M., and Furon, T · 2023
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What can discriminator do? towards box-free ownership verification of generative adversarial networks
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Razzhigaev, A., Shakhmatov, A., Maltseva, A., Arkhipkin, V., Pavlov, I., Ryabov, I., Kuts, A., Panchenko, A., Kuznetsov, A., and Dimitrov, D · 2023
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Schramowski, P., Brack, M., Deiseroth, B., and Kersting, K · 2023
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Zou, A., Wang, Z., Kolter, J. Z., and Fredrikson, M · 2023
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Responsible generative ai: What to generate and what not
Gu, J · 2024
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Domain watermark: Effective and harmless dataset copyright protection is closed at hand
Guo, J., Li, Y., Wang, L., Xia, S.-T., Huang, H., Liu, C., and Li, B · 2024
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Jovanović, N., Staab, R., and Vechev, M · 2024
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Latent guard: a safety framework for text-to-image generation
Liu, R., Khakzar, A., Gu, J., Chen, Q., Torr, P., and Pizzati, F · 2024
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Finding needles in a haystack: A black-box approach to invisible watermark detection
Pan, M., Wang, Z., Dong, X., Sehwag, V., Lyu, L., and Lin, X · 2024
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Shao, S., Li, Y., Yao, H., He, Y., Qin, Z., and Ren, K · 2024
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Traceevader: Making deepfakes more untraceable via evading the forgery model attribution
Wu, M., Ma, J., Wang, R., Zhang, S., Liang, Z., Li, B., Lin, C., Fang, L., and Wang, L · 2024
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