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Adversarial attacks against Deep Neural Networks have been widely studied.
ABBA: Saliency-Regularized Motion-Based Adversarial Blur Attack
Guo, Q.; Juefei-Xu, F.; Xie, X.; Ma, L.; Wang, J.; Feng, W.; and Liu, Y. 2020a · 2002
Earlier work this paper cites.
Towards characterizing adversarial defects of deep learning software from the lens of uncertainty
Zhang, X.; Xie, X.; Ma, L.; Du, X.; Hu, Q.; Liu, Y.; Zhao, J.; and Sun, M. 2020 · 2004
Earlier work this paper cites.
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack
Cheng, Y.; Guo, Q.; Juefei-Xu, F.; Xie, X.; Lin, S.-W.; Lin, W.; Feng, W.; and Liu, Y. 2020a · 2007
Earlier work this paper cites.
Adversarial Exposure Attack on Diabetic Retinopathy Imagery
Cheng, Y.; Juefei-Xu, F.; Guo, Q.; Fu, H.; Xie, X.; Lin, S.-W.; Lin, W.; and Liu, Y. 2020b · 2009
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
Earlier work this paper cites.
Making Images Undiscoverable from Co-Saliency Detection
Gao, R.; ; Guo, Q.; Juefei-Xu, F.; Yu, H.; Ren, X.; Feng, W.; and Wang, S. 2020 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
Earlier work this paper cites.
Bias Field Poses a Threat to DNN-based X-Ray Recognition
Tian, B.; Guo, Q.; Juefei-Xu, F.; Chan, W.; Cheng, Y.; Li, X.; Xie, X.; and Qin, S. 2020 · 2009
Earlier work this paper cites.
It’s Raining Cats or Dogs? Adversarial Rain Attack on DNN Perception
Zhai, L.; Juefei-Xu, F.; Guo, Q.; Xie, X.; Ma, L.; Feng, W.; Qin, S.; and Liu, Y. 2020 · 2009
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2013 · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
Cited alongside, same era.
Learning with a strong adversary
Huang, R.; Xu, B.; Schuurmans, D.; and Szepesvári, C. 2015 · 2015
Cited alongside, same era.
Adversarial examples in the physical world
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2016 · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Liu, Y.; Chen, X.; Liu, C.; and Song, D. 2016 · 2016
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Stealing hyperparameters in machine learning
Wang, B.; and Gong, N. Z. 2018 · 2018
Later among the works it cites.
Understanding and enhancing the transferability of adversarial examples
Wu, L.; Zhu, Z.; Tai, C.; et al. 2018 · 2018
Later among the works it cites.
Transferable adversarial perturbations
Zhou, W.; Hou, X.; Chen, Y.; Tang, M.; Huang, X.; Gan, X.; and Yang, Y. 2018 · 2018
Later among the works it cites.
Evading defenses to transferable adversarial examples by translation-invariant attacks
Dong, Y.; Pang, T.; Su, H.; and Zhu, J. 2019 · 2019
Later among the works it cites.
Adversarial examples are not bugs, they are features
Ilyas, A.; Santurkar, S.; Tsipras, D.; Engstrom, L.; Tran, B.; and Madry, A. 2019 · 2019
Later among the works it cites.
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Papernot, N.; McDaniel, P.; and Goodfellow, I. 2016 · 2016
Cited alongside, same era.
”Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
Cited alongside, same era.
Stealing Machine Learning Models via Prediction APIs
Tramèr, F.; Zhang, F.; Juels, A.; Reiter, M. K.; and Ristenpart, T. 2016 · 2016
Cited alongside, same era.
Densely connected convolutional networks
Huang, G.; Liu, Z.; Van Der Maaten, L.; and Weinberger, K. Q. 2017 · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A.; Carlini, N.; and Wagner, D. 2018 · 2018
Cited alongside, same era.
Boosting Adversarial Attacks With Momentum
Dong, Y.; Liao, F.; Pang, T.; Su, H.; Zhu, J.; Hu, X.; and Li, J. 2018 · 2018
Cited alongside, same era.
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Shaham, U.; Yamada, Y.; and Negahban, S. 2018 · 2018
Cited alongside, same era.
SPARK: Spatial-aware online incremental attack against visual tracking
Guo, Q.; Xie, X.; Juefei-Xu, F.; Ma, L.; Li, Z.; Xue, W.; Feng, W.; and Liu, Y. 2020b
Cited in the paper.
DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems
Ma, L.; Juefei-Xu, F.; Xue, M.; Li, B.; Li, L.; Liu, Y.; and Zhao, J. 2019 · 2019
Later among the works it cites.
A Discussion of ’Adversarial Examples Are Not Bugs, They Are Features’: Adversarial Examples are Just Bugs, Too
Nakkiran, P. 2019 · 2019
Later among the works it cites.
High Accuracy and High Fidelity Extraction of Neural Networks
Jagielski, M.; Carlini, N.; Berthelot, D.; Kurakin, A.; and Papernot, N. 2020 · 2020
Closest in time.
Amora: Black-box Adversarial Morphing Attack
Wang, R.; Juefei-Xu, F.; Guo, Q.; Huang, Y.; Xie, X.; Ma, L.; and Liu, Y. 2020 · 2020
Closest in time.
CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples
Yu, H.; Yang, K.; Zhang, T.; Tsai, Y.-Y.; Ho, T.-Y.; and Jin, Y. 2020 · 2020
Closest in time.