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Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models.
Exploiting Machine Learning to Subvert Your Spam Filter
Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, enjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. Doug Tygar, and Kai Xia · 2008
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An Analysis of Single-Layer Networks in Unsupervised Feature Learning
Adam Coates, Andrew Y. Ng, and Honglak Lee · 2011
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Reading Digits in Natural Images with Unsupervised Feature Learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings
Bo Li and Yevgeniy Vorobeychik · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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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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Visualizing Large-scale and High-dimensional Data
Jian Tang, Jingzhou Liu, Ming Zhang, and Qiaozhu Mei · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 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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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Neural trojans
Yuntao Liu, Yang Xie, and Ankur Srivastava · 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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Machine Learning Models that Remember Too Much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Trojaning Attack on Neural Networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2018
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SoK: Towards the Science of Security and Privacy in Machine Learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2018
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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
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Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Recent advances in algorithmic high-dimensional robust statistics
Ilias Diakonikolas and Daniel M Kane · 2019
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Robust anomaly detection and backdoor attack detection via differential privacy
Min Du, Ruoxi Jia, and Dawn Song · 2019
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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
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DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model
Xiang Ling, Shouling Ji, Jiaxu Zou, Jiannan Wang, Chunming Wu, Bo Li, and Ting Wang · 2019
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ABS: Scanning Neural Networks for Back-Doors by Artificial Brain Stimulation
Yingqi Liu, Wen-Chuan Lee, Guanhong Tao, Shiqing Ma, Yousra Aafer, and Xiangyu Zhang · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Deep probabilistic models to detect data poisoning attacks
Mahesh Subedar, Nilesh Ahuja, Ranganath Krishnan, Ibrahima J Ndiour, and Omesh Tickoo · 2019
Cited alongside, same era.
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2019
Cited alongside, same era.
Latent Backdoor Attacks on Deep Neural Networks
Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y. Zhao · 2019
Cited alongside, same era.
Neural attention distillation: Erasing backdoor triggers from deep neural networks
Yige Li, Nodens Koren, Lingjuan Lyu, Xixiang Lyu, Bo Li, and Xingjun Ma · 2021
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Anti-backdoor learning: Training clean models on poisoned data
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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Invisible backdoor attack with sample-specific triggers
Yuezun Li, Yiming Li, Baoyuan Wu, Longkang Li, Ran He, and Siwei Lyu · 2021
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Removing backdoor-based watermarks in neural networks with limited data
Xuankai Liu, Fengting Li, Bihan Wen, and Qi Li · 2021
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Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2019
Cited alongside, same era.
DAPAS: Denoising autoencoder to prevent adversarial attack in semantic segmentation
Seungju Cho, Tae Joon Jun, Byungsoo Oh, and Daeyoung Kim · 2020
Cited alongside, same era.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems
Bao Gia Doan, Ehsan Abbasnejad, and Damith C. Ranasinghe · 2020
Cited alongside, same era.
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham · 2020
Cited alongside, same era.
Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization
Shaofeng Li, Minhui Xue, Benjamin Zi Hao Zhao, Haojin Zhu, and Xinpeng Zhang · 2020
Cited alongside, same era.
Yiming Li, Baoyuan Wu, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2020
Cited alongside, same era.
Deep k-nn defense against clean-label data poisoning attacks
Neehar Peri, Neal Gupta, W Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, and John P Dickerson · 2020
Cited alongside, same era.
Mohammad Malekzadeh, Anastasia Borovykh, and Deniz Gündüz · 2021
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Dataset Meta-Learning from Kernel Ridge-Regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2021
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Dataset Distillation with Infinitely Wide Convolutional Networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
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You autocomplete me: Poisoning vulnerabilities in neural code completion
Roei Schuster, Congzheng Song, Eran Tromer, and Vitaly Shmatikov · 2021
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Backdoor pre-trained models can transfer to all
Lujia Shen, Shouling Ji, Xuhong Zhang, Jinfeng Li, Jing Chen, Jie Shi, Chengfang Fang, Jianwei Yin, and Ting Wang · 2021
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Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection
Di Tang, XiaoFeng Wang, Haixu Tang, and Kehuan Zhang · 2021
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Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks
Lin Wang and Kuk-Jin Yoon · 2021
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Graph Backdoor
Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang · 2021
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A backdoor attack against 3d point cloud classifiers
Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, and George Kesidis · 2021
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Crfl: Certifiably robust federated learning against backdoor attacks
Chulin Xie, Minghao Chen, Pin-Yu Chen, and Bo Li · 2021
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Detecting AI Trojans Using Meta Neural Analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A. Gunter, and Bo Li · 2021
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Backdoor Attacks to Graph Neural Networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2021
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Dataset Condensation with Differentiable Siamese Augmentatio
Bo Zhao and Hakan Bilen · 2021
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Dataset Condensation with Distribution Matching
Bo Zhao and Hakan Bilen · 2021
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Dataset Condensation With Gradient Matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Topological detection of trojaned neural networks
Songzhu Zheng, Yikai Zhang, Hubert Wagner, Mayank Goswami, and Chao Chen · 2021
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Poisoning and backdooring contrastive learning
Nicholas Carlini and Andreas Terzis · 2022
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Dataset Distillation by Matching Training Trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
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Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, and Tom Goldstein · 2022
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FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, and Yue Liu · 2022
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BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong · 2022
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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
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Dynamic Backdoor Attacks Against Machine Learning Models
Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, and Yang Zhang · 2022
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Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
Rui Song, Dai Liu, Dave Zhenyu Chen, Andreas Festag, Carsten Trinitis, Martin Schulz, and Alois C. Knoll · 2022
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FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, and Cho-Jui Hsieh · 2022
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