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Model inversion and membership inference attacks aim to reconstruct and verify the data which a model was trained on.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2017
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Data-free knowledge distillation for deep neural networks
Raphael Gontijo Lopes, Stefano Fenu, and Thad Starner · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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Model inversion attacks against collaborative inference
Zecheng He, Tianwei Zhang, and Ruby B Lee · 2019
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Monte carlo and reconstruction membership inference attacks against generative models
Benjamin Hilprecht, Martin Härterich, and Daniel Bernau · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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White-box vs black-box: Bayes optimal strategies for membership inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou · 2019
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Neural network inversion in adversarial setting via background knowledge alignment
Ziqi Yang, Jiyi Zhang, Ee-Chien Chang, and Zhenkai Liang · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Gan-leaks: A taxonomy of membership inference attacks against generative models
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
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Generative low-bitwidth data free quantization
Shoukai Xu, Haokun Li, Bohan Zhuang, Jing Liu, Jiezhang Cao, Chuangrun Liang, and Mingkui Tan · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
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Qimera: Data-free quantization with synthetic boundary supporting samples
Kanghyun Choi, Deokki Hong, Noseong Park, Youngsok Kim, and Jinho Lee · 2021
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Fedpara: Low-rank hadamard product for communication-efficient federated learning
Nam Hyeon-Woo, Moon Ye-Bin, and Tae-Hyun Oh · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Fine-tuning global model via data-free knowledge distillation for non-iid federated learning
Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, and Ling-Yu Duan · 2022
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Break-a-scene: Extracting multiple concepts from a single image
Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel Cohen-Or, and Dani Lischinski · 2023
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Emu: Enhancing image generation models using photogenic needles in a haystack
Xiaoliang Dai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, et al · 2023
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Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2023
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Are diffusion models vulnerable to membership inference attacks?
Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, and Kaidi Xu · 2023
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Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Membership inference attacks are easier on difficult problems
Avital Shafran, Shmuel Peleg, and Yedid Hoshen · 2021
Cited alongside, same era.
On the importance of difficulty calibration in membership inference attacks
Lauren Watson, Chuan Guo, Graham Cormode, and Alex Sablayrolles · 2021
Cited alongside, same era.
Data-free knowledge distillation for heterogeneous federated learning
Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou · 2021
Cited alongside, same era.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
Cited alongside, same era.
Up to 100x faster data-free knowledge distillation
Gongfan Fang, Kanya Mo, Xinchao Wang, Jie Song, Shitao Bei, Haofei Zhang, and Mingli Song · 2022
Cited alongside, same era.
Reconstructing training data from trained neural networks
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, and Michal Irani · 2022
Cited alongside, same era.
Membership inference attacks on machine learning: A survey
Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S Yu, and Xuyun Zhang · 2022
Cited alongside, same era.
Later among the works it cites.
Membership inference of diffusion models
Hailong Hu and Jun Pang · 2023
Later among the works it cites.
Multi-concept customization of text-to-image diffusion
Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu · 2023
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Psaq-vit v2: Toward accurate and general data-free quantization for vision transformers
Zhikai Li, Mengjuan Chen, Junrui Xiao, and Qingyi Gu · 2023
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Gpt understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2023
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Membership inference attacks against diffusion models
Tomoya Matsumoto, Takayuki Miura, and Naoto Yanai · 2023
Later among the works it cites.
Re-thinking model inversion attacks against deep neural networks
Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, and Ngai-Man Cheung · 2023
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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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Data-free knowledge distillation for fine-grained visual categorization
Renrong Shao, Wei Zhang, Jianhua Yin, and Jun Wang · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al · 2023
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Recovering the pre-fine-tuning weights of generative models
Eliahu Horwitz, Jonathan Kahana, and Yedid Hoshen · 2024
Closest in time.
Students parrot their teachers: Membership inference on model distillation
Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A Choquette-Choo, Nicholas Carlini, and Florian Tramer · 2024
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Objectdrop: Bootstrapping counterfactuals for photorealistic object removal and insertion, 2024
Daniel Winter, Matan Cohen, Shlomi Fruchter, Yael Pritch, Alex Rav-Acha, and Yedid Hoshen · 2024
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