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Pre-trained encoders are general-purpose feature extractors that can be used for many downstream tasks.
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges. 2010 · 2010
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An analysis of single-layer networks in unsupervised feature learning. In AISTATS
Adam Coates, Andrew Ng, and Honglak Lee. 2011 · 2011
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Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. 2011 · 2011
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel. 2012 · 2012
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Food-101 – Mining Discriminative Components with Random Forests. In ECCV
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. 2014 · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures. In CCS
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Deep residual learning for image recognition. In CVPR
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Stealing machine learning models via prediction apis. In USENIX Security Symposium
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. 2016 · 2016
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Membership inference attacks against machine learning models. In IEEE S&P
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017 · 2017
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Turning your weakness into a strength: Watermarking deep neural networks by backdooring. In USENIX Security Symposium
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. 2018 · 2018
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Towards Reverse-Engineering Black-Box Neural Networks. In ICLR
Seong Joon Oh, Max Augustin, Mario Fritz, and Bernt Schiele. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Stealing hyperparameters in machine learning. In IEEE S&P
Binghui Wang and Neil Zhenqiang Gong. 2018 · 2018
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Protecting intellectual property of deep neural networks with watermarking. In AsiaCCS
Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph Stoecklin, Heqing Huang, and Ian Molloy. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Memguard: Defending against black-box membership inference attacks via adversarial examples. In CCS
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong. 2019 · 2019
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PRADA: protecting against DNN model stealing attacks. In EuroS&P
Mika Juuti, Sebastian Szyller, Samuel Marchal, and N Asokan. 2019 · 2019
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Thieves on sesame street! model extraction of bert-based apis. In ICLR
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P Parikh, Nicolas Papernot, and Mohit Iyyer. 2019 · 2019
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Knockoff nets: Stealing functionality of black-box models. In CVPR
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Language models are few-shot learners. In NeurIPS
Poisoning and Backdooring Contrastive Learning
Nicholas Carlini and Andreas Terzis. 2021 · 2021
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Extracting training data from large language models. In USENIX Security Symposium
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Stealing links from graph neural networks. In USENIX Security Symposium
Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. 2021 · 2021
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Quantifying and Mitigating Privacy Risks of Contrastive Learning. In CCS
Xinlei He and Yang Zhang. 2021 · 2021
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10 Security and Privacy Problems in Self-Supervised Learning
Jinyuan Jia, Hongbin Liu, and Neil Zhenqiang Gong. 2021b · 2021
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Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Cryptanalytic extraction of neural network models. In CRYPTO
Nicholas Carlini, Matthew Jagielski, and Ilya Mironov. 2020 · 2020
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Exploring connections between active learning and model extraction. In USENIX Security Symposium
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, and Songbai Yan. 2020 · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations. In ICML
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning. In CVPR
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
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High accuracy and high fidelity extraction of neural networks. In USENIX Security Symposium
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot. 2020 · 2020
Cited alongside, same era.
Defending against model stealing attacks with adaptive misinformation. In CVPR
Sanjay Kariyappa and Moinuddin K Qureshi. 2020 · 2020
Cited alongside, same era.
Badencoder: Backdoor attacks to pre-trained encoders in self-supervised learning
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong. 2021c · 2021
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Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes. In AsiaCCS
Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong. 2021 · 2021
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Maze: Data-free model stealing attack using zeroth-order gradient estimation. In CVPR
Sanjay Kariyappa, Atul Prakash, and Moinuddin K Qureshi. 2021 · 2021
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EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning. In CCS
Hongbin Liu, Jinyuan Jia, Wenjie Qu, and Neil Zhenqiang Gong. 2021 · 2021
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SoK: How Robust is Image Classification Deep Neural Network Watermarking?
Nils Lukas, Edward Jiang, Xinda Li, and Florian Kerschbaum. 2021a · 2021
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Learning transferable visual models from natural language supervision. In ICML
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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Grey-box extraction of natural language models. In ICML
Santiago Zanella-Beguelin, Shruti Tople, Andrew Paverd, and Boris Köpf. 2021 · 2021
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SEAT: Similarity Encoder by Adversarial Training for Detecting Model Extraction Attack Queries. In AISec
Zhanyuan Zhang, Yizheng Chen, and David Wagner. 2021 · 2021
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Hermes Attack: Steal DNN Models with Lossless Inference Accuracy. In USENIX Security Symposium
Yuankun Zhu, Yueqiang Cheng, Husheng Zhou, and Yantao Lu. 2021 · 2021
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Clarifai General Image Embedding Model
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Clarifai Price Sheet
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SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders
Tianshuo Cong, Xinlei He, and Yang Zhang. 2022 · 2022
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PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning. In USENIX Security Symposium
Hongbin Liu, Jinyuan Jia, and Neil Zhenqiang Gong. 2022 · 2022
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