Fetching the paper…
Reading the bibliography…
Deep neural networks are being widely deployed for many critical tasks due to their high classification accuracy.
The influence curve and its role in robust estimation
Frank R Hampel · 1974
Earlier work this paper cites.
Tracking and object classification for automated surveillance
Omar Javed and Mubarak Shah · 2002
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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 · 2014
Earlier work this paper cites.
Deepid3: Face recognition with very deep neural networks
Yi Sun, Ding Liang, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Early methods for detecting adversarial images
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Earlier work this paper cites.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Earlier work this paper cites.
On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel · 2017
Earlier work this paper cites.
On detecting adversarial perturbations
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff · 2017
Earlier work this paper cites.
Detecting adversarial samples from artifacts
Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner · 2017
Earlier work this paper cites.
Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
Cited alongside, same era.
Safetynet: Detecting and rejecting adversarial examples robustly
Jiajun Lu, Theerasit Issaranon, and David Forsyth · 2017
Cited alongside, same era.
The rise of deep learning in drug discovery
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, and Thomas Blaschke · 2018
Cited alongside, same era.
Seq3seq fingerprint: towards end-to-end semi-supervised deep drug discovery
Xiaoyu Zhang, Sheng Wang, Feiyun Zhu, Zheng Xu, Yuhong Wang, and Junzhou Huang · 2018
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
Later among the works it cites.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
Later among the works it cites.
Sentinet: Detecting physical attacks against deep learning systems
Edward Chou, Florian Tramèr, Giancarlo Pellegrino, and Dan Boneh · 2018
Later among the works it cites.
Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li · 2019
Later among the works it cites.
Badnets: Evaluating backdooring attacks on deep neural networks
T. Gu, K. Liu, B. Dolan-Gavitt, and S. Garg · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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 · 2018
Cited alongside, same era.
Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
Cited alongside, same era.
Defense against universal adversarial perturbations
Naveed Akhtar, Jian Liu, and Ajmal Mian · 2018
Cited alongside, same era.
Art of singular vectors and universal adversarial perturbations
Valentin Khrulkov and Ivan Oseledets · 2018
Cited alongside, same era.
Defense against universal adversarial perturbations
Naveed Akhtar, Jian Liu, and Ajmal Mian · 2018
Cited alongside, same era.
Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2018
Cited alongside, same era.
Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 2019
Later among the works it cites.
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
Later among the works it cites.
Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks
Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar · 2019
Later among the works it cites.
Neuroninspect: Detecting backdoors in neural networks via output explanations
Xijie Huang, Moustafa Alzantot, and Mani Srivastava · 2019
Later among the works it cites.
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
Later among the works it cites.
Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
Later among the works it cites.
A survey on neural trojans
Yuntao Liu, Ankit Mondal, Abhishek Chakraborty, Michael Zuzak, Nina Jacobsen, Daniel Xing, and Ankur Srivastava · 2020
Closest in time.