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Visualizing Higher-Layer Features of a Deep Network
Dumitru Erhan, Y. Bengio, Aaron Courville, and Pascal Vincent · 2009
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2014
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler and Rob Fergus · 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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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Understanding Neural Networks Through Deep Visualization, 2015
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 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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Visualizing Deep Convolutional Neural Networks Using Natural Pre-images
Aravindh Mahendran and Andrea Vedaldi · 2016
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Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks, 2016
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2016
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Aggregated residual transformations for deep neural networks, 2017
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 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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Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter · 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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BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain, 2019
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 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.
Understanding Neural Networks via Feature Visualization: A Survey
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 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.
End-to-End Object Detection with Transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks
Alexander Levine and Soheil Feizi · 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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Backdoor Scanning for Deep Neural Networks through K-Arm Optimization
Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu, Siyuan Cheng, Shiqing Ma, and Xiangyu Zhang · 2021
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Adversarial Neuron Pruning Purifies Backdoored Deep Models
Dongxian Wu and Yisen Wang · 2021
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SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, and Ping Luo · 2021
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Detecting AI Trojans Using Meta Neural Analysis
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
Cited alongside, same era.
One-Pixel Signature: Characterizing CNN Models for Backdoor Detection
Shanjiaoyang Huang, Weiqi Peng, Zhiwei Jia, and Zhuowen Tu · 2020
Cited alongside, same era.
The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models, 2020
Kiran Karra, Chace Ashcraft, and Neil Fendley · 2020
Cited alongside, same era.
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash, and Heiko Hoffmann · 2020
Cited alongside, same era.
Input-Aware Dynamic Backdoor Attack
Tuan Anh Nguyen and Anh Tran · 2020
Cited alongside, same era.
Hidden Trigger Backdoor Attacks
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 2020
Cited alongside, same era.
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong, and Meng Wang · 2020
Cited alongside, same era.
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A. Gunter, and Bo Li · 2021
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Witches’ Brew: Industrial Scale Data Poisoning via Gradient Matching
Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein · 2022
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Label-Only Model Inversion Attacks via Boundary Repulsion
Mostafa Kahla, Si Chen, Hoang Anh Just, and Ruoxi Jia · 2022
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Complex Backdoor Detection by Symmetric Feature Differencing
Yingqi Liu, Guangyu Shen, Guanhong Tao, Zhenting Wang, Shiqing Ma, and Xiangyu Zhang · 2022
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Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch
Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, and Tom Goldstein · 2022
Later among the works it cites.
Variational Model Inversion Attacks
Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, and Alireza Makhzani · 2022
Later among the works it cites.
Defending Backdoor Attacks on Vision Transformer via Patch Processing
Khoa D. Doan, Yingjie Lao, Peng Yang, and Ping Li · 2023
Closest in time.
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Mądry, Bo Li, and Tom Goldstein · 2023
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MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic
Hang Wang, Zhen Xiang, David J. Miller, and George Kesidis · 2023
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RAB: Provable Robustness Against Backdoor Attacks
Maurice Weber, Xiaojun Xu, Bojan Karlas, Ce Zhang, and Bo Li · 2023
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