Stealing Machine Learning Models via Prediction APIs

9 September 2016

Papers citing "Stealing Machine Learning Models via Prediction APIs"

50 / 351 papers shown

Title
PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks K. Mandal G. Gong FedML 14 71 0 05 Apr 2020
FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning Sameer Wagh Shruti Tople Fabrice Benhamouda E. Kushilevitz Prateek Mittal T. Rabin FedML 38 296 0 05 Apr 2020
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies David Enthoven Zaid Al-Ars FedML 60 50 0 01 Apr 2020
DaST: Data-free Substitute Training for Adversarial Attacks Mingyi Zhou Jing Wu Yipeng Liu Shuaicheng Liu Ce Zhu 30 142 0 28 Mar 2020
Learn to Forget: Machine Unlearning via Neuron Masking Yang Liu Zhuo Ma Ximeng Liu Jian-wei Liu Zhongyuan Jiang Jianfeng Ma Philip Yu K. Ren MU 30 61 0 24 Mar 2020
Backdooring and Poisoning Neural Networks with Image-Scaling Attacks Erwin Quiring Konrad Rieck AAML 54 70 0 19 Mar 2020
Dynamic Backdoor Attacks Against Machine Learning Models A. Salem Rui Wen Michael Backes Shiqing Ma Yang Zhang AAML 66 271 0 07 Mar 2020
Entangled Watermarks as a Defense against Model Extraction Hengrui Jia Christopher A. Choquette-Choo Varun Chandrasekaran Nicolas Papernot WaLM AAML 15 218 0 27 Feb 2020
SNIFF: Reverse Engineering of Neural Networks with Fault Attacks J. Breier Dirmanto Jap Xiaolu Hou S. Bhasin Yang Liu 17 53 0 23 Feb 2020
Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Apps Zhichuang Sun Ruimin Sun Long Lu Alan Mislove 39 78 0 18 Feb 2020
How to 0wn NAS in Your Spare Time Sanghyun Hong Michael Davinroy Yigitcan Kaya Dana Dachman-Soled Tudor Dumitras 33 36 0 17 Feb 2020
Adversarial Machine Learning -- Industry Perspectives Ramnath Kumar Magnus Nyström J. Lambert Andrew Marshall Mario Goertzel Andi Comissoneru Matt Swann Sharon Xia AAML SILM 29 232 0 04 Feb 2020
On the Resilience of Biometric Authentication Systems against Random Inputs Benjamin Zi Hao Zhao Hassan Jameel Asghar M. Kâafar AAML 39 23 0 13 Jan 2020
Assessing differentially private deep learning with Membership Inference Daniel Bernau Philip-William Grassal J. Robl Florian Kerschbaum MIACV FedML 26 23 0 24 Dec 2019
Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker Nicholas Roberts Vinay Uday Prabhu Matthew McAteer 24 18 0 19 Dec 2019
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples Nils Lukas Yuxuan Zhang Florian Kerschbaum MLAU FedML AAML 39 145 0 02 Dec 2019
Privacy Leakage Avoidance with Switching Ensembles R. Izmailov Peter Lin Chris Mesterharm S. Basu 27 2 0 18 Nov 2019
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey Olakunle Ibitoye Rana Abou-Khamis Mohamed el Shehaby Ashraf Matrawy M. O. Shafiq AAML 47 68 0 06 Nov 2019
Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks Y. Sagduyu Yi Shi T. Erpek AAML 33 83 0 01 Nov 2019
IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary Xiaoyu Cao Jinyuan Jia Neil Zhenqiang Gong 19 106 0 28 Oct 2019
Secure Evaluation of Quantized Neural Networks Anders Dalskov Daniel E. Escudero Marcel Keller 22 138 0 28 Oct 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
Representation Learning for Electronic Health Records W. Weng Peter Szolovits 36 19 0 19 Sep 2019
VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service Lingchen Zhao Qian Wang Cong Wang Qi Li Chao Shen Xiaodong Lin Bo Feng Minxin Du VLM 13 86 0 16 Sep 2019
Detection of Backdoors in Trained Classifiers Without Access to the Training Set Zhen Xiang David J. Miller G. Kesidis AAML 30 23 0 27 Aug 2019
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems Lea Schonherr Thorsten Eisenhofer Steffen Zeiler Thorsten Holz D. Kolossa AAML 54 63 0 05 Aug 2019
Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems Alessandro Erba Riccardo Taormina S. Galelli Marcello Pogliani Michele Carminati S. Zanero Nils Ole Tippenhauer AAML 28 22 0 17 Jul 2019
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks Tribhuvanesh Orekondy Bernt Schiele Mario Fritz AAML 19 164 0 26 Jun 2019
Adversarial Examples to Fool Iris Recognition Systems Sobhan Soleymani Ali Dabouei J. Dawson Nasser M. Nasrabadi GAN AAML 24 16 0 21 Jun 2019
Membership Privacy for Machine Learning Models Through Knowledge Transfer Virat Shejwalkar Amir Houmansadr 22 10 0 15 Jun 2019
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks Felipe A. Mejia Paul Gamble Z. Hampel-Arias M. Lomnitz Nina Lopatina Lucas Tindall M. Barrios SILM 27 18 0 15 Jun 2019
Adversarial Exploitation of Policy Imitation Vahid Behzadan W. Hsu 27 24 0 03 Jun 2019
Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks Sanghyun Hong Pietro Frigo Yigitcan Kaya Cristiano Giuffrida Tudor Dumitras AAML 22 211 0 03 Jun 2019
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference Peichen Xie Bingzhe Wu Guangyu Sun BDL FedML 19 33 0 03 Jun 2019
Bypassing Backdoor Detection Algorithms in Deep Learning T. Tan Reza Shokri FedML AAML 39 149 0 31 May 2019
Misleading Authorship Attribution of Source Code using Adversarial Learning Erwin Quiring Alwin Maier Konrad Rieck 19 107 0 29 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 6 235 0 24 May 2019
Zero-shot Knowledge Transfer via Adversarial Belief Matching P. Micaelli Amos Storkey 19 228 0 23 May 2019
A framework for the extraction of Deep Neural Networks by leveraging public data Soham Pal Yash Gupta Aditya Shukla Aditya Kanade S. Shevade V. Ganapathy FedML MLAU MIACV 36 56 0 22 May 2019
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks David J. Miller Zhen Xiang G. Kesidis AAML 19 35 0 12 Apr 2019
Differentially Private Model Publishing for Deep Learning Lei Yu Ling Liu C. Pu Mehmet Emre Gursoy Stacey Truex FedML 15 264 0 03 Apr 2019
Data Poisoning against Differentially-Private Learners: Attacks and Defenses Yuzhe Ma Xiaojin Zhu Justin Hsu SILM 25 157 0 23 Mar 2019
Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints Xing Hu Ling Liang Lei Deng Shuangchen Li Xinfeng Xie Yu Ji Yufei Ding Chang Liu T. Sherwood Yuan Xie AAML MLAU 23 36 0 10 Mar 2019
Copying Machine Learning Classifiers Irene Unceta Jordi Nin O. Pujol 14 18 0 05 Mar 2019
Attacking Graph-based Classification via Manipulating the Graph Structure Binghui Wang Neil Zhenqiang Gong AAML 41 155 0 01 Mar 2019
Evaluating Differentially Private Machine Learning in Practice Bargav Jayaraman David Evans 15 7 0 24 Feb 2019
XONN: XNOR-based Oblivious Deep Neural Network Inference M. Riazi Mohammad Samragh Hao Chen Kim Laine Kristin E. Lauter F. Koushanfar FedML GNN BDL 31 280 0 19 Feb 2019
On the security relevance of weights in deep learning Kathrin Grosse T. A. Trost Marius Mosbach Michael Backes Dietrich Klakow AAML 32 6 0 08 Feb 2019
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study Xurong Li S. Ji Men Han Juntao Ji Zhenyu Ren Yushan Liu Chunming Wu AAML 29 31 0 04 Jan 2019
Stealing Neural Networks via Timing Side Channels Vasisht Duddu D. Samanta D. V. Rao V. Balas AAML MLAU FedML 33 133 0 31 Dec 2018