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The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Recent trends in image watermarking techniques for copyright protection: a survey
Arkadip Ray and Somaditya Roy · 2020
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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 · 2020
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Some light infringement?
David Chess · 2022
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BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
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Pokemon blip captions
Justin N. M. Pinkney · 2022
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A self-supervised descriptor for image copy detection
Ed Pizzi, Sreya Dutta Roy, Sugosh Nagavara Ravindra, Priya Goyal, and Matthijs Douze · 2022
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Hierarchical text-conditional image generation with CLIP latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Red-teaming the stable diffusion safety filter
Javier Rando, Daniel Paleka, David Lindner, Lennard Heim, and Florian Tramèr · 2022
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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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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
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Formalizing human ingenuity: A quantitative framework for copyright law’s substantial similarity
Sarah Scheffler, Eran Tromer, and Mayank Varia · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Hossein Aboutalebi, Daniel Mao, Carol Xu, and Alexander Wong · 2023
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Instructpix2pix: Learning to follow image editing instructions
Tim Brooks, Aleksander Holynski, and Alexei A Efros · 2023
Cited alongside, same era.
Extracting training data from diffusion models
Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
Cited alongside, same era.
Measuring the success of diffusion models at imitating human artists
Stephen Casper, Zifan Guo, Shreya Mogulothu, Zachary Marinov, Chinmay Deshpande, Rui-Jie Yew, Zheng Dai, and Dylan Hadfield-Menell · 2023
Cited alongside, same era.
Trojdiff: Trojan attacks on diffusion models with diverse targets
Weixin Chen, Dawn Song, and Bo Li · 2023
Cited alongside, same era.
Prompting4debugging: Red-teaming text-to-image diffusion models by finding problematic prompts
Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang, Pin-Yu Chen, and Wei-Chen Chiu · 2023
Cited alongside, same era.
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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Cones: Concept neurons in diffusion models for customized generation
Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, and Yang Cao · 2023
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Adversarial prompting for black box foundation models
Natalie Maus, Patrick Chao, Eric Wong, and Jacob Gardner · 2023
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Protecting the intellectual property of diffusion models by the watermark diffusion process
Sen Peng, Yufei Chen, Cong Wang, and Xiaohua Jia · 2023
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Unsafe diffusion: On the generation of unsafe images and hateful memes from text-to-image models
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How to backdoor diffusion models?
Sheng-Yen Chou, Pin-Yu Chen, and Tsung-Yi Ho · 2023
Cited alongside, same era.
Diffusionshield: A watermark for copyright protection against generative diffusion models
Yingqian Cui, Jie Ren, Han Xu, Pengfei He, Hui Liu, Lichao Sun, and Jiliang Tang · 2023
Cited alongside, same era.
Towards more realistic membership inference attacks on large diffusion models
Jan Dubiński, Antoni Kowalczuk, Stanisław Pawlak, Przemysław Rokita, Tomasz Trzciński, and Paweł Morawiecki · 2023
Cited alongside, same era.
Can copyright be reduced to privacy?
Niva Elkin-Koren, Uri Hacohen, Roi Livni, and Shay Moran · 2023
Cited alongside, same era.
An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit Haim Bermano, Gal Chechik, and Daniel Cohen-Or · 2023
Cited alongside, same era.
Erasing concepts from diffusion models
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, and David Bau · 2023
Cited alongside, same era.
Evaluating the robustness of text-to-image diffusion models against real-world attacks
Hongcheng Gao, Hao Zhang, Yinpeng Dong, and Zhijie Deng · 2023
Cited alongside, same era.
Yiting Qu, Xinyue Shen, Xinlei He, Michael Backes, Savvas Zannettou, and Yang Zhang · 2023
Closest in time.
Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Raising the cost of malicious ai-powered image editing
Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, and Aleksander Madry · 2023
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Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting · 2023
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Diffusion art or digital forgery? investigating data replication in diffusion models
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Ring-a-bell! how reliable are concept removal methods for diffusion models?
Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin, Jia-You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, and Chun-Ying Huang · 2023
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On provable copyright protection for generative models
Nikhil Vyas, Sham M Kakade, and Boaz Barak · 2023
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On the proactive generation of unsafe images from text-to-image models using benign prompts
Yixin Wu, Ning Yu, Michael Backes, Yun Shen, and Yang Zhang · 2023
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Duaw: Data-free universal adversarial watermark against stable diffusion customization
Xiaoyu Ye, Hao Huang, Jiaqi An, and Yongtao Wang · 2023
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Text-to-image diffusion models can be easily backdoored through multimodal data poisoning
Shengfang Zhai, Yinpeng Dong, Qingni Shen, Shi Pu, Yuejian Fang, and Hang Su · 2023
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Forget-me-not: Learning to forget in text-to-image diffusion models
Eric Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, and Humphrey Shi · 2023
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A recipe for watermarking diffusion models
Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Ngai-Man Cheung, and Min Lin · 2023
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A pilot study of query-free adversarial attack against stable diffusion
Haomin Zhuang, Yihua Zhang, and Sijia Liu · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson · 2023
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