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Deep Neural Networks (DNNs) are ubiquitous and span a variety of applications ranging from image classification to real-time object detection.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Speech recognition with deep recurrent neural networks
A. Graves, Abdel rahman Mohamed, and Geoffrey E. Hinton · 2013
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Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 2013
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Explaining and harnessing adversarial examples
I. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deep face recognition
O. Parkhi, A. Vedaldi, and Andrew Zisserman · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and Andrew Zisserman · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sergey Zagoruyko and N. Komodakis · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and D. Song · 2017
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Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, L. Chen, M. Kounavis, and Duen Horng Chau · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2017
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A survey on deep learning in medical image analysis
G. Litjens, Thijs Kooi, B. E. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. V. D. Laak, B. Ginneken, and C. Sánchez · 2017
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Neural trojans
Yuntao Liu, Yang Xie, and Ankur Srivastava · 2017
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Deep reinforcement learning framework for autonomous driving
Ahmad El Sallab, Mohammed Abdou, E. Perot, and S. Yogamani · 2017
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Shield: Fast, practical defense and vaccination for deep learning using jpeg compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, L. Chen, M. Kounavis, and Duen Horng Chau · 2018
Earlier work this paper cites.
Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
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A new backdoor attack in cnns by training set corruption without label poisoning
M. Barni, K. Kallas, and B. Tondi · 2019
Earlier work this paper cites.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Ben Edwards, Taesung Lee, Ian Molloy, and B. Srivastava · 2019
Cited alongside, same era.
Strip: a defence against trojan attacks on deep neural networks
Yansong Gao, Chang Xu, Derui Wang, Shiping Chen, Damith Chinthana Ranasinghe, and Surya Nepal · 2019
Cited alongside, same era.
Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, K. Liu, Brendan Dolan-Gavitt, and S. Garg · 2019
Cited alongside, same era.
Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems
Wenbo Guo, L. Wang, Xinyu Xing, Min Du, and D. Song · 2019
Cited alongside, same era.
Abs: Scanning neural networks for back-doors by artificial brain stimulation
Yingqi Liu, Wen-Chuan Lee, Guanhong Tao, Shiqing Ma, Yousra Aafer, and X. Zhang · 2019
Backdoor attack with imperceptible input and latent modification
Khoa D Doan and Yingjie Lao · 2021
Closest in time.
Lira: Learnable, imperceptible and robust backdoor attacks
Khoa D Doan, Yingjie Lao, Weijie Zhao, and Ping Li · 2021
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Fiba: Frequency-injection based backdoor attack in medical image analysis
Yu Feng, Benteng Ma, Jing Zhang, Shanshan Zhao, Yong Xia, and Dacheng Tao · 2021
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Spectre: Defending against backdoor attacks using robust statistics
Jonathan Hayase, Weihao Kong, Raghav Somani, and Sewoong Oh · 2021
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Artificial intelligence security: Threats and countermeasures
Yupeng Hu, Wenxin Kuang, Zheng Qin, Kenli Li, Jiliang Zhang, Yansong Gao, Wenjia Li, and Keqin Li · 2021
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Cited alongside, same era.
Defending neural backdoors via generative distribution modeling
Ximing Qiao, Yukun Yang, and H. Li · 2019
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2019
Cited alongside, same era.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, B. Viswanath, Haitao Zheng, and Ben Y. Zhao · 2019
Cited alongside, same era.
A fourier perspective on model robustness in computer vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, E. D. Cubuk, and J. Gilmer · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Hongyang R. Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, and Michael I. Jordan · 2019
Cited alongside, same era.
Badnl: Backdoor attacks against nlp models
Xiaoyi Chen, A. Salem, Michael Backes, Shiqing Ma, and Yang Zhang · 2020
Cited alongside, same era.
Februus: Input purification defense against trojan attacks on deep neural network systems
Bao Gia Doan, Ehsan Abbasnejad, and D. Ranasinghe · 2020
Cited alongside, same era.
Todd P. Huster and Emmanuel Ekwedike · 2021
Closest in time.
Highly accurate protein structure prediction with alphafold
J. Jumper, Richard Evans, A. Pritzel, Tim Green, Michael Figurnov, O. Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zídek, Anna Potapenko, A. Bridgland, Clemens Meyer, Simon A A Kohl, Andy Ballard, A. Cowie, B. Romera-Paredes, Stanislav Nikolov, Rishub Jain, J. Adler, T. Back, Stig Petersen, D. Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, S. Bodenstein, D. Silver, Oriol Vinyals, A. Senior, K. Kavukcuoglu, P. Kohli, and D. Hassabis · 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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Wanet - imperceptible warping-based backdoor attack
A. Nguyen and A. Tran · 2021
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Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks
Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, and Tom Goldstein · 2021
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Backdoor attack through frequency domain
Tong Wang, Yuan Yao, Feng Xu, Shengwei An, Hanghang Tong, and Ting Wang · 2021
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Backdoor attacks against deep learning systems in the physical world
Emily Wenger, Josephine Passananti, Arjun Nitin Bhagoji, Yuanshun Yao, Haitao Zheng, and Ben Y. Zhao · 2021
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Dehib: Deep hidden backdoor attack on semi-supervised learning via adversarial perturbation
Zhicong Yan, Gaolei Li, Yuan Tian, Jun Wu, Shenghong Li, Mingzhe Chen, and H. Vincent Poor · 2021
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Rethinking the backdoor attacks’ triggers: A frequency perspective
Yi Zeng, Won Park, Zhuoqing Morley Mao, and R. Jia · 2021
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Poison ink: Robust and invisible backdoor attack
Jie zhang, Dongdong Chen, Jing Liao, Qidong Huang, G. Hua, Weiming Zhang, and Nenghai Yu · 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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Blindnet backdoor: Attack on deep neural network using blind watermark
Hyung-Min Kwon and Yongchul Kim · 2022
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Invisible backdoor attacks on deep neural networks via steganography and regularization
Shaofeng Li, Minhui Xue, Benjamin Zi Hao Zhao, Haojin Zhu, and Xinpeng Zhang · 2088
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