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Machine learning (ML) has made tremendous progress during the past decade and is being adopted in various critical real-world applications.
Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
Earlier work this paper cites.
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
Earlier work this paper cites.
Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Optimal Randomized Classification in Adversarial Settings
Yevgeniy Vorobeychik and Bo Li · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep Learning Face Attributes in the Wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
Earlier work this paper cites.
Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
Earlier work this paper cites.
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Badnets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Grag · 2017
Earlier work this paper cites.
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
Earlier work this paper cites.
Adversarial Image Perturbation for Privacy Protection – A Game Theory Perspective
Seong Joon Oh, Mario Fritz, and Bernt Schiele · 2017
Earlier work this paper cites.
Practical Black-Box Attacks Against Machine Learning
Nicolas Papernot, Patrick D. McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
Earlier work this paper cites.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Earlier work this paper cites.
Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
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.
Understanding Membership Inferences on Well-Generalized Learning Models
Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A. Gunter, and Kai Chen · 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.
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
Cited alongside, same era.
Latent Backdoor Attacks on Deep Neural Networks
Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y. Zhao · 2019
Later among the works it cites.
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
Closest in time.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems
Bao Gia Doan, Ehsan Abbasnejad, and Damith C. Ranasinghe · 2020
Closest in time.
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu · 2020
Closest in time.
Input-Aware Dynamic Backdoor Attack
Tuan Anh Nguyen and Anh Tran · 2020
Closest in time.
TBT: Targeted Neural Network Attack with Bit Trojan
Adnan Siraj Rakin, Zhezhi He, and Deliang Fan · 2020
Closest in time.
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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.
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.
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks
Yansong Gao, Change Xu, Derui Wang, Shiping Chen, Damith C Ranasinghe, and Surya Nepal · 2019
Cited alongside, same era.
MBeacon: Privacy-Preserving Beacons for DNA Methylation Data
Inken Hagestedt, Yang Zhang, Mathias Humbert, Pascal Berrang, Haixu Tang, XiaoFeng Wang, and Michael Backes · 2019
Cited alongside, same era.
Hidden Trigger Backdoor Attacks
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 2020
Closest in time.
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, and Yang Zhang · 2020
Closest in time.
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu · 2020
Closest in time.
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
Closest in time.
Clean-Label Backdoor Attacks on Video Recognition Models
Shihao Zhao, Xingjun Ma, Xiang Zheng, James Bailey, Jingjing Chen, and Yu-Gang Jiang · 2020
Closest in time.
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
Closest in time.
Stealing Links from Graph Neural Networks
Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang · 2021
Closest in time.
Quantifying and Mitigating Privacy Risks of Contrastive Learning
Xinlei He and Yang Zhang · 2021
Closest in time.
Detecting AI Trojans Using Meta Neural Analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A. Gunter, and Bo Li · 2021
Closest in time.
BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong · 2022
Closest in time.
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Yugeng Liu, Rui Wen, Xinlei He, Ahmed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, and Yang Zhang · 2022
Closest in time.
Get a Model! Model Hijacking Attack Against Machine Learning Models
Ahmed Salem, Michael Backes, and Yang Zhang · 2022
Closest in time.
Model Stealing Attacks Against Inductive Graph Neural Networks
Yun Shen, Xinlei He, Yufei Han, and Yang Zhang · 2022
Closest in time.
Property Inference Attacks Against GANs
Junhao Zhou, Yufei Chen, Chao Shen, and Yang Zhang · 2022
Closest in time.