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Text-to-image generative models, especially those based on latent diffusion models (LDMs), have demonstrated outstanding ability in generating high-quality and high-resolution images from textual prompts.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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Digital watermarking: algorithms and applications
Christine I Podilchuk and Edward J Delp · 2001
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Digital watermarking
Ingemar Cox, Matthew Miller, Jeffrey Bloom, and Chris Honsinger · 2002
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Cryptography & network security
Behrouz A Forouzan · 2007
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Dwt-dct-svd based watermarking
KA Navas, Mathews Cheriyan Ajay, M Lekshmi, Tampy S Archana, and M Sasikumar · 2008
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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U-net: Convolutional networks for biomedical image segmentation, 2015
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Hidden: Hiding data with deep networks
Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei · 2018
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Diffusers: State-of-the-art diffusion models
Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, and Thomas Wolf · 2022
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A pathway towards responsible ai generated content
Chen Chen, Jie Fu, and Lingjuan Lyu · 2023
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Adversarial example does good: preventing painting imitation from diffusion models via adversarial examples
Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, and Haibing Guan · 2023
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Generative watermarking against unauthorized subject-driven image synthesis
Yihan Ma, Zhengyu Zhao, Xinlei He, Zheng Li, Michael Backes, and Yang Zhang · 2023
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Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Wanet–imperceptible warping-based backdoor attack
Anh Nguyen and Anh Tran · 2021
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Feature purification: How adversarial training performs robust deep learning
Zeyuan Allen-Zhu and Yuanzhi Li · 2022
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An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Diagnosis: Detecting unauthorized data usages in text-to-image diffusion models
Zhenting Wang, Chen Chen, Lingjuan Lyu, Dimitris Metaxas, and Shiqing Ma
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
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Raising the cost of malicious ai-powered image editing
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Glaze: Protecting artists from style mimicry by text-to-image models
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Anti-dreambooth: Protecting users from personalized text-to-image synthesis
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Detecting, explaining, and mitigating memorization in diffusion models
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Copyright protection in generative ai: A technical perspective
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