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Backdoor attacks represent one of the major threats to machine learning models.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity Mappings in Deep Residual Networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian M. Molloy, and Biplav Srivastava · 2018
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Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
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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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Machine Learning with Membership Privacy using Adversarial Regularization
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
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Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates
Leslie N. Smith and Nicholay Topin · 2018
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Spectral Signatures in Backdoor Attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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A New Backdoor Attack in CNNS by Training Set Corruption Without Label Poisoning
Mauro Barni, Kassem Kallas, and Benedetta Tondi · 2019
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DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks
Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar · 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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TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems
Wenbo Guo, Lun Wang, Xinyu Xing, Min Du, and Dawn Song · 2019
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NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations
Xijie Huang, Moustafa Alzantot, and Mani B. Srivastava · 2019
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Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, and Quoc V. Le · 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.
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 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.
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
Liwei Song, Reza Shokri, and Prateek Mittal · 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.
Clean-Label Backdoor Attacks on Video Recognition Models
Shihao Zhao, Xingjun Ma, Xiang Zheng, James Bailey, Jingjing Chen, and Yu-Gang Jiang · 2020
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Exploring Simple Siamese Representation Learning
Xinlei Chen and Kaiming He · 2021
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Label-Only Membership Inference Attacks
Christopher A. Choquette Choo, Florian Tramèr, Nicholas Carlini, and Nicolas Papernot · 2021
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Masked Autoencoders Are Scalable Vision Learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross B. Girshick · 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
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Latent Backdoor Attacks on Deep Neural Networks
Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y. Zhao · 2019
Cited alongside, same era.
A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2019
Cited alongside, same era.
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
Cited alongside, same era.
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
Cited alongside, same era.
Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
Cited alongside, same era.
Supervised Contrastive Learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
Cited alongside, same era.
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference
Klas Leino and Matt Fredrikson · 2020
Cited alongside, same era.
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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Membership Leakage in Label-Only Exposures
Zheng Li and Yang Zhang · 2021
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WaNet - Imperceptible Warping-based Backdoor Attack
Tuan Anh Nguyen and Anh Tuan Tran · 2021
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Systematic Evaluation of Privacy Risks of Machine Learning Models
Liwei Song and Prateek Mittal · 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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Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspective
Yi Zeng, Won Park, Z. Morley Mao, and Ruoxi Jia · 2021
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Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models
Xinlei He, Zheng Li, Weilin Xu, Cory Cornelius, and Yang Zhang · 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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Backdoor Attacks in the Supply Chain of Masked Image Modeling
Xinyue Shen, Xinlei He, Zheng Li, Yun Shen, Michael Backes, and Yang Zhang · 2022
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Model Agnostic Defence Against Backdoor Attacks in Machine Learning
Sakshi Udeshi, Shanshan Peng, Gerald Woo, Lionell Loh, Louth Rawshan, and Sudipta Chattopadhyay · 2022
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BackdoorBench: A Comprehensive Benchmark of Backdoor Learning
Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Chao Shen, and Hongyuan Zha · 2022
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