Fetching the paper…
Reading the bibliography…
Machine learning models deployed as a service (MLaaS) are susceptible to model stealing attacks, where an adversary attempts to steal the model within a restricted access framework.
The unreasonable effectiveness of data
Alon Halevy, Peter Norvig, and Fernando Pereira · 2009
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Romero Adriana, Ballas Nicolas, K Samira Ebrahimi, Chassang Antoine, Gatta Carlo, and B Yoshua · 2015
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Data-free knowledge distillation for deep neural networks
Raphael Gontijo Lopes, Stefano Fenu, and Thad Starner · 2017
Earlier work this paper cites.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Earlier work this paper cites.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Earlier work this paper cites.
Copycat cnn: Stealing knowledge by persuading confession with random non-labeled data
Jacson Rodrigues Correia-Silva, Rodrigo F Berriel, Claudine Badue, Alberto F de Souza, and Thiago Oliveira-Santos · 2018
Earlier work this paper cites.
Defending against model stealing attacks using deceptive perturbations
Taesung Lee, Benjamin Edwards, Ian Molloy, and Dong Su · 2018
Cited alongside, same era.
Transferable adversarial perturbations
Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang · 2018
Cited alongside, same era.
Compressing GANs using knowledge distillation
Angeline Aguinaldo, Ping-Yeh Chiang, Alex Gain, Ameya Patil, Kolten Pearson, and Soheil Feizi · 2019
Cited alongside, same era.
Data-free learning of student networks
Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian · 2019
Cited alongside, same era.
Data-free adversarial distillation
Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song · 2019
Cited alongside, same era.
Defending against model stealing attacks with adaptive misinformation
Sanjay Kariyappa and Moinuddin K Qureshi · 2020
Later among the works it cites.
Model compression with two-stage multi-teacher knowledge distillation for web question answering system
Ze Yang, Linjun Shou, Ming Gong, Wutao Lin, and Daxin Jiang · 2020
Later among the works it cites.
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
Later among the works it cites.
The secret revealer: Generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2020
Later among the works it cites.
Dast: Data-free substitute training for adversarial attacks
Mingyi Zhou, Jing Wu, Yipeng Liu, Shuaicheng Liu, and Ce Zhu · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Thieves on sesame street! model extraction of bert-based apis
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P Parikh, Nicolas Papernot, and Mohit Iyyer · 2019
Cited alongside, same era.
Model reconstruction from model explanations
Smitha Milli, Ludwig Schmidt, Anca D Dragan, and Moritz Hardt · 2019
Cited alongside, same era.
Zero-shot knowledge distillation in deep networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty · 2019
Cited alongside, same era.
Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
Cited alongside, same era.
Prediction poisoning: Towards defenses against dnn model stealing attacks
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
Cited alongside, same era.
A framework for the extraction of deep neural networks by leveraging public data
Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, and Vinod Ganapathy · 2019
Cited alongside, same era.
DeGAN: Data-enriching gan for retrieving representative samples from a trained classifier
Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, and Venkatesh Babu Radhakrishnan · 2020
Cited alongside, same era.
Varun Chandrasekaran, Hengrui Jia, Anvith Thudi, Adelin Travers, Mohammad Yaghini, and Nicolas Papernot · 2021
Later among the works it cites.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
Later among the works it cites.
Maze: Data-free model stealing attack using zeroth-order gradient estimation
Sanjay Kariyappa, Atul Prakash, and Moinuddin K Qureshi · 2021
Later among the works it cites.
Data-free model extraction
Jean-Baptiste Truong, Pratyush Maini, Robert J Walls, and Nicolas Papernot · 2021
Later among the works it cites.
Delving into data: Effectively substitute training for black-box attack
Wenxuan Wang, Bangjie Yin, Taiping Yao, Li Zhang, Yanwei Fu, Shouhong Ding, Jilin Li, Feiyue Huang, and Xiangyang Xue · 2021
Later among the works it cites.
Black-box dissector: Towards erasing-based hard-label model stealing attack
Yixu Wang, Jie Li, Hong Liu, Yan Wang, Yongjian Wu, Feiyue Huang, and Rongrong Ji · 2021
Later among the works it cites.
Zero-shot knowledge distillation from a decision-based black-box model
Zi Wang · 2021
Later among the works it cites.
Exploiting explanations for model inversion attacks
Xuejun Zhao, Wencan Zhang, Xiaokui Xiao, and Brian Y Lim · 2021
Later among the works it cites.