Defending Against Machine Learning Model Stealing Attacks Using
Deceptive Perturbations

Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations

31 May 2018

Papers citing "Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations"

11 / 11 papers shown

Title
Careful What You Wish For: on the Extraction of Adversarially Trained Models Kacem Khaled Gabriela Nicolescu F. Magalhães MIACV AAML 35 4 0 21 Jul 2022
Mitigating Black-Box Adversarial Attacks via Output Noise Perturbation Manjushree B. Aithal Xiaohua Li AAML 60 6 0 30 Sep 2021
Proof-of-Learning: Definitions and Practice Hengrui Jia Mohammad Yaghini Christopher A. Choquette-Choo Natalie Dullerud Anvith Thudi Varun Chandrasekaran Nicolas Papernot AAML 25 99 0 09 Mar 2021
Automated Discovery of Adaptive Attacks on Adversarial Defenses Chengyuan Yao Pavol Bielik Petar Tsankov Martin Vechev AAML 19 24 0 23 Feb 2021
Stealing Deep Reinforcement Learning Models for Fun and Profit Kangjie Chen Shangwei Guo Tianwei Zhang Xiaofei Xie Yang Liu MLAU MIACV OffRL 24 45 0 09 Jun 2020
Perturbing Inputs to Prevent Model Stealing J. Grana AAML SILM 24 5 0 12 May 2020
MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation Sanjay Kariyappa A. Prakash Moinuddin K. Qureshi AAML 32 146 0 06 May 2020
Imitation Attacks and Defenses for Black-box Machine Translation Systems Eric Wallace Mitchell Stern D. Song AAML 27 120 0 30 Apr 2020
Entangled Watermarks as a Defense against Model Extraction Hengrui Jia Christopher A. Choquette-Choo Varun Chandrasekaran Nicolas Papernot WaLM AAML 13 218 0 27 Feb 2020
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples Nils Lukas Yuxuan Zhang Florian Kerschbaum MLAU FedML AAML 39 145 0 02 Dec 2019
Extraction of Complex DNN Models: Real Threat or Boogeyman? B. Atli S. Szyller Mika Juuti Samuel Marchal Nadarajah Asokan MLAU MIACV 33 45 0 11 Oct 2019