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Due to insufficient training data and the high computational cost to train a deep neural network from scratch, transfer learning has been extensively used in many deep-neural-network-based applications.
Security of deep learning methodologies: Challenges and opportunities
Shahbaz Rezaei and Xin Liu · 1912
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Earlier work this paper cites.
Deep face recognition
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, et al · 2015
Earlier work this paper cites.
Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
Earlier work this paper cites.
Attention to scale: Scale-aware semantic image segmentation
Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, and Alan L Yuille · 2016
Earlier work this paper cites.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
Cited alongside, same era.
Toward open-set face recognition
Manuel Gunther, Steve Cruz, Ethan M Rudd, and Terrance E Boult · 2017
Cited alongside, same era.
Blocking transferability of adversarial examples in black-box learning systems
Hossein Hosseini, Yize Chen, Sreeram Kannan, Baosen Zhang, and Radha Poovendran · 2017
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 · 2017
Cited alongside, same era.
Towards poisoning of deep learning algorithms with back-gradient optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C Lupu, and Fabio Roli · 2017
Cited alongside, same era.
Model-reuse attacks on deep learning systems
Yujie Ji, Xinyang Zhang, Shouling Ji, Xiapu Luo, and Ting Wang · 2018
Later among the works it cites.
Transfer learning from speaker verification to multispeaker text-to-speech synthesis
Ye Jia, Yu Zhang, Ron Weiss, Quan Wang, Jonathan Shen, Fei Ren, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, Yonghui Wu, et al · 2018
Later among the works it cites.
Backdoor embedding in convolutional neural network models via invisible perturbation
Cong Liao, Haoti Zhong, Anna Squicciarini, Sencun Zhu, and David Miller · 2018
Later among the works it cites.
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
Later among the works it cites.
With great training comes great vulnerability: practical attacks against transfer learning
Bolun Wang, Yuanshun Yao, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2018
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
The extreme value machine
Ethan M Rudd, Lalit P Jain, Walter J Scheirer, and Terrance E Boult · 2017
Cited alongside, same era.
Are you tampering with my data?
Michele Alberti, Vinaychandran Pondenkandath, Marcel Wursch, Manuel Bouillon, Mathias Seuret, Rolf Ingold, and Marcus Liwicki · 2018
Cited alongside, same era.
Adversarial examples that fool both human and computer vision
Gamaleldin F Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, and Jascha Sohl-Dickstein · 2018
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner
Cited in the paper.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh
Cited in the paper.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song
Cited in the paper.
Later among the works it cites.
URL http://vis-www.cs.umass.edu/lfw/
Labeled faces in the wild, 2016 · 2019
Closest in time.
URL https://www.tensorflow.org/tutorials/sequences/audio_recognition
Speech commands, 2017 · 2019
Closest in time.
URL https://github.com/pannous/tensorflow-speech-recognition
Pannous speech recognition, 2017 · 2019
Closest in time.
How to achieve high classification accuracy with just a few labels: A semi-supervised approach using sampled packets
Shahbaz Rezaei and Xin Liu · 2019
Closest in time.