BODAME: Bilevel Optimization for Defense Against Model Extraction

[1]  Christopher Meek,et al.  Adversarial learning , 2005, KDD '05.

[2]  Ankur P. Parikh,et al.  Thieves on Sesame Street! Model Extraction of BERT-based APIs , 2019, ICLR.

[3]  Vinod Ganapathy,et al.  ActiveThief: Model Extraction Using Active Learning and Unannotated Public Data , 2020, AAAI.

[4]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[5]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[6]  Akiko Takeda,et al.  Solving the Trust-Region Subproblem By a Generalized Eigenvalue Problem , 2017, SIAM J. Optim..

[7]  Paolo Frasconi,et al.  Bilevel Programming for Hyperparameter Optimization and Meta-Learning , 2018, ICML.

[8]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[9]  Tribhuvanesh Orekondy,et al.  Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks , 2020, ICLR.

[10]  Somesh Jha,et al.  Exploring Connections Between Active Learning and Model Extraction , 2018, USENIX Security Symposium.

[11]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[12]  Lejla Batina,et al.  CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel , 2019, USENIX Security Symposium.

[13]  Anders Forsgren,et al.  Interior Methods for Nonlinear Optimization , 2002, SIAM Rev..

[14]  Seong Joon Oh,et al.  Towards Reverse-Engineering Black-Box Neural Networks , 2017, ICLR.

[15]  Paolo Frasconi,et al.  Forward and Reverse Gradient-Based Hyperparameter Optimization , 2017, ICML.

[16]  R. Horst,et al.  Global Optimization: Deterministic Approaches , 1992 .

[17]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[18]  Osbert Bastani,et al.  Interpretability via Model Extraction , 2017, ArXiv.

[19]  David Rolnick,et al.  Reverse-engineering deep ReLU networks , 2019, ICML.

[20]  Somesh Jha,et al.  Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.

[21]  Anca D. Dragan,et al.  Model Reconstruction from Model Explanations , 2018, FAT.

[22]  Chao Yang,et al.  ARPACK users' guide - solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods , 1998, Software, environments, tools.

[23]  Ryan P. Adams,et al.  Gradient-based Hyperparameter Optimization through Reversible Learning , 2015, ICML.

[24]  Ian Molloy,et al.  Defending Against Neural Network Model Stealing Attacks Using Deceptive Perturbations , 2019, 2019 IEEE Security and Privacy Workshops (SPW).

[25]  David Berthelot,et al.  High Accuracy and High Fidelity Extraction of Neural Networks , 2020, USENIX Security Symposium.

[26]  Chengfang Fang,et al.  BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks , 2019, ESORICS.

[27]  Fan Zhang,et al.  Stealing Machine Learning Models via Prediction APIs , 2016, USENIX Security Symposium.

[28]  Xiangliang Zhang,et al.  Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering , 2014, CIKM.

[29]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[30]  Tribhuvanesh Orekondy,et al.  Knockoff Nets: Stealing Functionality of Black-Box Models , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Vijay Arya,et al.  Model Extraction Warning in MLaaS Paradigm , 2017, ACSAC.

[32]  Jonathon Shlens,et al.  Explaining and Harnessing Adversarial Examples , 2014, ICLR.

[33]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[34]  Samuel Marchal,et al.  PRADA: Protecting Against DNN Model Stealing Attacks , 2018, 2019 IEEE European Symposium on Security and Privacy (EuroS&P).