[20] Hamed Karimi, Julie Nutini, and Mark Schmidt. Linear convergence of gradient and proximal-gradient methods under the polyak-lojasiewicz condition. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I 16, pages 795-811. Springer, 2016.
[21] Sanjay Kariyappa, Atul Prakash, and Moinuddin K Qureshi. Maze: Data-free model stealing attack using zeroth-order gradient estimation. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[22] Sanjay Kariyappa, Atul Prakash, and Moinuddin K Qureshi. Protecting {dnn}s from theft using an ensemble of diverse models. In International Conference on Learning Representations, 2021.
[23] Sanjay Kariyappa and Moinuddin K Qureshi. Defending against model stealing attacks with adaptive misinformation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
[24] Jangho Kim, SeongUk Park, and Nojun Kwak. Paraphrasing complex network: network compression via factor transfer. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, pages 2765-2774, 2018.
[25] Alex Krizhevsky. Learning multiple layers of features from tiny images, 2009.
[26] Taesung Lee, Benjamin Edwards, Ian Molloy, and Dong Su. Defending against machine learning model stealing attacks using deceptive perturbations, 2018.
[27] Guanlin Li, Guowen Xu, Shangwei Guo, Han Qiu, Jiwei Li, and Tianwei Zhang. Extracting robust models with uncertain examples. In The Eleventh International Conference on Learning Representations, 2023.
[28] Huiying Li, Shawn Shan, Emily Wenger, Jiayun Zhang, Haitao Zheng, and Ben Y. Zhao. Blacklight: Scalable defense for neural networks against query-based black-box attacks. In 2022 Conference 31th USENIX security symposium (USENIX Security 22), 2022.
[29] Qiang Liu. Stein variational gradient descent as gradient flow. Advances in Neural Information Processing Systems, 2017.
[30] Sijia Liu, Pin-Yu Chen, Bhavya Kaikhura, Gaoyuan Zhang, Alfred O. Hero III, and Pramod K. Varshney. A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications. IEEE Signal Processing Magazine, 2020.
[31] Daniel Lowd and Christopher Meek. Adversarial learning. In KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, page 641-647. Association for Computing Machinery, 2005.
[32] Pratyush Maini, Mohammad Yaghini, and Nicolas Papernot. Dataset inference: Ownership resolution in machine learning. In International Conference on Learning Representations, 2021.
[33] Mantas Mazeika, Bo Li, and David Forsyth. How to steer your adversary: Targeted and efficient model stealing defenses with gradient redirection. 2022.
[34] Erich Miersemann. Calculus of Variations, Lecture Notes. Leipzig University, 2012.
[35] Seong Joon Oh, Max Augustin, Bernt Schiele, and Mario Fritz. Towards reverse-engineering black-box neural networks. International Conference on Learning Representations, 2018.
[36] Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. Knockoff nets: Stealing functionality of black-box models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[37] Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. Knockoff nets: Stealing functionality of black-box models. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4949-4958, 2019.
[38] Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. Prediction poisoning: Towards defenses against dnn model stealing attacks. In International Conference on Learning Representations, 2020.
[39] Soham Pal, Yash Gupta, Aditya Kanade, and Shirish Shevade. Stateful detection of model extraction attacks, 2021.
[40] Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, and Vinod Ganapathy. Activethief: Model extraction using active learning and unannotated public data. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 2020.