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Deep learning models are being integrated into a wide range of high-impact, security-critical systems, from self-driving cars to medical diagnosis.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in CVPR , 2009
2009
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results.”
2010
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in ICLR , 2014
2014
Earlier work this paper cites.
X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille, “Detect what you can: Detecting and representing objects using holistic models and body parts,” in CVPR , 2014
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in IEEE Symposium on Security and Privacy . IEEE, 2016, pp. 582–597
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
F. Hohman, N. Hodas, and D. H. Chau, “Shapeshop: Towards understanding deep learning representations via interactive experimentation,” in CHI , 2017
2017
Earlier work this paper cites.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in CCS , 2017
2017
Earlier work this paper cites.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” in International Conference on Learning Representations , 2017
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
R. Shin and D. Song, “Jpeg-resistant adversarial images,” NIPS 2017 Workshop on Machine Learning and Computer Security , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
B. Liang, H. Li, M. Su, X. Li, W. Shi, and X. Wang, “Detecting adversarial examples in deep networks with adaptive noise reduction,” arXiv preprint , 2017
2017
Cited alongside, same era.
F. Carrara, F. Falchi, R. Caldelli, G. Amato, R. Fumarola, and R. Becarelli, “Detecting adversarial example attacks to deep neural networks,” ser. CBMI, 2017
2018
Later among the works it cites.
M. Alzantot, Y. Sharma, S. Chakraborty, and M. B. Srivastava, “Genattack: Practical black-box attacks with gradient-free optimization,” arXiv preprint , 2018
2018
Later among the works it cites.
A. Ilyas, L. Engstrom, A. Athalye, and J. Lin, “Black-box adversarial attacks with limited queries and information,” in ICML , 2018
2018
Later among the works it cites.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in ICLR , 2018
2018
Later among the works it cites.
Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in CVPR , 2018
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2017
Cited alongside, same era.
X. Li and F. Li, “Adversarial examples detection in deep networks with convolutional filter statistics.” in ICCV , 2017, pp. 5775–5783
2017
Cited alongside, same era.
D. Meng and H. Chen, “Magnet: a two-pronged defense against adversarial examples,” in CSS , 2017
2017
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in ICCV , 2017
2017
Cited alongside, same era.
W. Abdulla, “Mask r-cnn for object detection and instance segmentation on keras and tensorflow,” https://github.com/matterport/Mask_RCNN , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
W. Brendel, J. Rauber, and M. Bethge, “Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,” in ICLR , 2018
2018
Cited alongside, same era.
S.-T. Chen, C. Cornelius, J. Martin, and D. H. Chau, “Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector,” in PKDD , 2018
2018
Cited alongside, same era.
2018
Later among the works it cites.
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel, “Ensemble adversarial training: Attacks and defenses,” in ICLR , 2018
2018
Later among the works it cites.
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, and D. H. Chau, “Shield: Fast, practical defense and vaccination for deep learning using jpeg compression,” in KDD , 2018
2018
Later among the works it cites.
A. Athalye, N. Carlini, and D. Wagner, “Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,” in Proceedings of the 35th International Conference on Machine Learning , 2018
2018
Later among the works it cites.
J. Wang, J. Sun, P. Zhang, and X. Wang, “Detecting adversarial samples for deep neural networks through mutation testing,” arXiv preprint , 2018
2018
Later among the works it cites.
W. Xu, D. Evans, and Y. Qi, “Feature squeezing: Detecting adversarial examples in deep neural networks,” in NDSS , 2018
2018
Later among the works it cites.
A. Turner, D. Tsipras, and A. Madry, “Clean-label backdoor attacks,” 2018
2018
Later among the works it cites.
2019
Later among the works it cites.