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Machine learning (ML) has established itself as a cornerstone for various critical applications ranging from autonomous driving to authentication systems.
Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing
Matt Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart · 2014
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Optimal Randomized Classification in Adversarial Settings
Yevgeniy Vorobeychik and Bo Li · 2014
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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings
Bo Li and Yevgeniy Vorobeychik · 2015
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Deep Learning Face Attributes in the Wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going Deeper with Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
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Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
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Badnets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Grag · 2017
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Adversarial Image Perturbation for Privacy Protection – A Game Theory Perspective
Seong Joon Oh, Mario Fritz, and Bernt Schiele · 2017
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Practical Black-Box Attacks Against Machine Learning
Nicolas Papernot, Patrick D. McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
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Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
Cited alongside, same era.
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
Jinyuan Jia and Neil Zhenqiang Gong · 2018
Cited alongside, same era.
Trojaning Attack on Neural Networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2018
Cited alongside, same era.
Towards Reverse-Engineering Black-Box Neural Networks
Seong Joon Oh, Max Augustin, Bernt Schiele, and Mario Fritz · 2018
Cited alongside, same era.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Knockoff Nets: Stealing Functionality of Black-Box Models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2019
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MnasNet: Platform-Aware Neural Architecture Search for Mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V. Le · 2019
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Latent Backdoor Attacks on Deep Neural Networks
Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y. Zhao · 2019
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Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu · 2020
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TBT: Targeted Neural Network Attack with Bit Trojan
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Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
Cited alongside, same era.
When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks
Octavian Suciu, Radu Mărginean, Yiğitcan Kaya, Hal Daumé III, and Tudor Dumitraş · 2018
Cited alongside, same era.
Data Poisoning Attack against Unsupervised Node Embedding Methods
Mingjie Sun, Jian Tang, Huichen Li, Bo Li, Chaowei Xiao, Yao Chen, and Dawn Song · 2018
Cited alongside, same era.
Stealing Hyperparameters in Machine Learning
Binghui Wang and Neil Zhenqiang Gong · 2018
Cited alongside, same era.
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
Cited alongside, same era.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
Tagvisor: A Privacy Advisor for Sharing Hashtags
Yang Zhang, Mathias Humbert, Tahleen Rahman, Cheng-Te Li, Jun Pang, and Michael Backes · 2018
Cited alongside, same era.
Adnan Siraj Rakin, Zhezhi He, and Deliang Fan · 2020
Later among the works it cites.
Hidden Trigger Backdoor Attacks
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 2020
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Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, and Yang Zhang · 2020
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Dynamic Backdoor Attacks Against Machine Learning Models
Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, and Yang Zhang · 2020
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You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion
Roei Schuster, Congzheng Song, Eran Tromer, and Vitaly Shmatikov · 2020
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An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu · 2020
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Data Poisoning Attacks Against Federated Learning Systems
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu · 2020
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Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos · 2020
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Practical Data Poisoning Attack against Next-Item Recommendation
Hengtong Zhang, Yaliang Li, Bolin Ding, and Jing Gao · 2020
Later among the works it cites.
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2020
Later among the works it cites.
Clean-Label Backdoor Attacks on Video Recognition Models
Shihao Zhao, Xingjun Ma, Xiang Zheng, James Bailey, Jingjing Chen, and Yu-Gang Jiang · 2020
Later among the works it cites.
BadNL: Backdoor Attacks Against NLP Models with Semantic-preserving Improvements
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, Qingni Shen, Zhonghai Wu, and Yang Zhang · 2021
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