Stealing Machine Learning Models via Prediction APIs

9 September 2016

Papers citing "Stealing Machine Learning Models via Prediction APIs"

50 / 344 papers shown

Title
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models Yugeng Liu Rui Wen Xinlei He A. Salem Zhikun Zhang Michael Backes Emiliano De Cristofaro Mario Fritz Yang Zhang AAML 17 125 0 04 Feb 2021
Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models? Jacson Rodrigues Correia-Silva Rodrigo Berriel C. Badue Alberto F. de Souza Thiago Oliveira-Santos MLAU 21 14 0 21 Jan 2021
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps Yujin Huang Han Hu Chunyang Chen AAML FedML 79 33 0 12 Jan 2021
Model Extraction and Defenses on Generative Adversarial Networks Hailong Hu Jun Pang SILM MIACV 33 14 0 06 Jan 2021
Practical Blind Membership Inference Attack via Differential Comparisons Bo Hui Yuchen Yang Haolin Yuan Philippe Burlina Neil Zhenqiang Gong Yinzhi Cao MIACV 35 120 0 05 Jan 2021
Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead Mohamed Bennai Mahum Naseer T. Theocharides C. Kyrkou O. Mutlu Lois Orosa Jungwook Choi OOD 81 100 0 04 Jan 2021
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead Maurizio Capra Beatrice Bussolino Alberto Marchisio Guido Masera Maurizio Martina Mohamed Bennai BDL 64 140 0 21 Dec 2020
Confidential Machine Learning on Untrusted Platforms: A Survey Sagar Sharma Keke Chen FedML 22 15 0 15 Dec 2020
Robustness Threats of Differential Privacy Nurislam Tursynbek Aleksandr Petiushko Ivan Oseledets AAML 35 13 0 14 Dec 2020
On Lightweight Privacy-Preserving Collaborative Learning for Internet of Things by Independent Random Projections Linshan Jiang Rui Tan Xin Lou Guosheng Lin 24 12 0 11 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 357 0 07 Dec 2020
Data-Free Model Extraction Jean-Baptiste Truong Pratyush Maini R. Walls Nicolas Papernot MIACV 15 181 0 30 Nov 2020
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack Rui Shu Tianpei Xia Laurie A. Williams Tim Menzies AAML 32 15 0 23 Nov 2020
SplitEasy: A Practical Approach for Training ML models on Mobile Devices Kamalesh Palanisamy Vivek Khimani Moin Hussain Moti Dimitris Chatzopoulos 27 20 0 09 Nov 2020
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA Adnan Siraj Rakin Yukui Luo Xiaolin Xu Deliang Fan AAML 25 49 0 05 Nov 2020
Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes Jinyuan Jia Binghui Wang Neil Zhenqiang Gong AAML 35 5 0 26 Oct 2020
Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization Bang Wu Xiangwen Yang Shirui Pan Xingliang Yuan MIACV MLAU 55 53 0 24 Oct 2020
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms Antonio Bărbălău Adrian Cosma Radu Tudor Ionescu Marius Popescu MIACV MLAU 30 43 0 21 Oct 2020
Amnesiac Machine Learning Laura Graves Vineel Nagisetty Vijay Ganesh MU MIACV 27 248 0 21 Oct 2020
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 33 157 0 08 Sep 2020
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding Sahar Abdelnabi Mario Fritz WaLM 28 143 0 07 Sep 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 33 51 0 03 Sep 2020
Simulating Unknown Target Models for Query-Efficient Black-box Attacks Chen Ma L. Chen Junhai Yong MLAU OOD 41 17 0 02 Sep 2020
POSEIDON: Privacy-Preserving Federated Neural Network Learning Sinem Sav Apostolos Pyrgelis J. Troncoso-Pastoriza D. Froelicher Jean-Philippe Bossuat João Sá Sousa Jean-Pierre Hubaux FedML 21 153 0 01 Sep 2020
Deep-Lock: Secure Authorization for Deep Neural Networks Manaar Alam Sayandeep Saha Debdeep Mukhopadhyay S. Kundu 14 21 0 13 Aug 2020
Membership Leakage in Label-Only Exposures Zheng Li Yang Zhang 34 237 0 30 Jul 2020
SOTERIA: In Search of Efficient Neural Networks for Private Inference Anshul Aggarwal Trevor E. Carlson Reza Shokri Shruti Tople FedML 27 12 0 25 Jul 2020
Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review Yansong Gao Bao Gia Doan Zhi-Li Zhang Siqi Ma Jiliang Zhang Anmin Fu Surya Nepal Hyoungshick Kim AAML 40 221 0 21 Jul 2020
A Survey of Privacy Attacks in Machine Learning M. Rigaki Sebastian Garcia PILM AAML 39 213 0 15 Jul 2020
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems H. Abdullah Kevin Warren Vincent Bindschaedler Nicolas Papernot Patrick Traynor AAML 32 128 0 13 Jul 2020
Quality Inference in Federated Learning with Secure Aggregation Balázs Pejó G. Biczók FedML 23 22 0 13 Jul 2020
The Trade-Offs of Private Prediction Laurens van der Maaten Awni Y. Hannun 25 22 0 09 Jul 2020
Generating Adversarial Examples with Controllable Non-transferability Renzhi Wang Tianwei Zhang Xiaofei Xie Lei Ma Cong Tian Felix Juefei Xu Yang Liu SILM AAML 17 3 0 02 Jul 2020
Legal Risks of Adversarial Machine Learning Research Ramnath Kumar J. Penney B. Schneier Kendra Albert AAML ELM SILM 22 8 0 29 Jun 2020
Hermes Attack: Steal DNN Models with Lossless Inference Accuracy Yuankun Zhu Yueqiang Cheng Husheng Zhou Yantao Lu MIACV AAML 39 99 0 23 Jun 2020
AdvMind: Inferring Adversary Intent of Black-Box Attacks Ren Pang Xinyang Zhang S. Ji Xiapu Luo Ting Wang MLAU AAML 11 29 0 16 Jun 2020
BoMaNet: Boolean Masking of an Entire Neural Network Anuj Dubey Rosario Cammarota Aydin Aysu AAML 25 45 0 16 Jun 2020
SPEED: Secure, PrivatE, and Efficient Deep learning Arnaud Grivet Sébert Rafael Pinot Martin Zuber Cédric Gouy-Pailler Renaud Sirdey FedML 15 20 0 16 Jun 2020
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
Revisiting Membership Inference Under Realistic Assumptions Bargav Jayaraman Lingxiao Wang Katherine Knipmeyer Quanquan Gu David Evans 24 147 0 21 May 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 33 83 0 18 May 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
When Machine Unlearning Jeopardizes Privacy Min Chen Zhikun Zhang Tianhao Wang Michael Backes Mathias Humbert Yang Zhang MIACV 36 218 0 05 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
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 78 25 0 27 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 33 295 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 25 142 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 24 61 0 24 Mar 2020