AI技术领域未来几年最引人瞩目的新方向是什么？( 四 ) _对抗

在机器学习发展飞速的今天，安全性问题正逐渐进入人们的的视野，对抗攻击不只能够在网络空间进行攻击，还能够在物理世界中任何使用到机器学习的场景中进行有效攻击，比如针对人脸识别、语音识别的攻击。为了机器学习更好的发展，研究对抗攻击是有必要的。因此我认为最近的科技新词是adversarial attack 。
5.引用
[1] N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, A.Swami, The Limitations of DeepLearning in Adversarial Settings, In Proceedings of IEEE European Symposium on Security andPrivacy, 2016.
[2] J. Su, D. V. Vargas, S. Kouichi, One pixel attack for fooling deep neural networks, arXiv preprintarXiv:1710.08864, 2017.
[3] S. Moosavi-Dezfooli, A. Fawzi, P. Frossard, DeepFool: a simple and accurate method to fooldeep neural networks, In Proceedings of the IEEE Conference on Computer Vision and PatternRecognition, pp. 2574-2582, 2016.
[4] C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, and A. Yuille, Adversarial Examples for SemanticSegmentation and Object Detection, arXiv preprint arXiv:1703.08603, 2017.
[5] Dai, Hanjun, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. "Adversarial Attackon Graph Structured Data." In International Conference on Machine Learning (ICML), vol. 2018.2018.
[6] Zu?gner, Daniel, Amir Akbarnejad, and Stephan Gu?nnemann. "Adversarial attacks on neuralnetworks for graph data." In Proceedings of the 24th ACM SIGKDD International Conference onKnowledge Discovery & Data Mining, pp. 2847-2856. ACM, 2018.
[7] Ying R, You J, Morris C, et al. Hierarchical graph representation learning with differentiable pooling[J]. CoRR, 2018
[8] Jia R, Liang P. Adversarial examples for evaluating reading comprehension systems[J]. arXiv preprint arXiv:1707.07328, 2017.
[9] Y. Lin, Z. Hong, Y. Liao, M. Shih, M. Liu, and M. Sun, Tactics of Adversarial Attack on DeepReinforcement Learning Agents, arXiv preprint arXiv:1703.06748, 2017.
[10] Papernot N, McDaniel P, Swami A, et al. Crafting adversarial input sequences for recurrent neural networks[C]//Military Communications Conference, MILCOM 2016-2016 IEEE. IEEE, 2016:49-54
[11] Carlini N, Wagner D. Audio adversarial examples: Targeted attacks on speech-to-text[J]. arXiv preprint arXiv:1801.01944, 2018.
[12] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguingproperties of neural networks, arXiv preprint arXiv:1312.6199, 2014.
[13] I. J. Goodfellow, J. Shlens, C. Szegedy, Explaining and Harnessing Adversarial Examples, arXivpreprint arXiv:1412.6572, 2015.
[14] Akhtar N, Mian A. Threat of adversarial attacks on deep learning in computer vision: A survey[J]. arXiv preprint arXiv:1801.00553, 2018
[15] Lu S, Yu L, Zhang W, et al. CoT: Cooperative Training for Generative Modeling[J]. arXiv preprint arXiv:1804.03782, 2018.
[16] Kingma D P, Dhariwal P. Glow: Generative flow with invertible 1x1 convolutions[J]. arXiv preprint arXiv:1807.03039, 2018.

AI技术领域未来几年最引人瞩目的新方向是什么？( 四 )

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