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GAN 기반 은닉 적대적 패치 생성 기법에 관한 연구  

Kim, Yongsu (부산대학교)
Kang, Hyoeun (부산대학교)
Kim, Howon (부산대학교)
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1 I. Goodfellow, et al., "Explaining and Harnessing Adversarial Examples," International Conference on Learning Representations, 2015.
2 A. Madrym et al., "Towards Deep Learning Models Resistant to Adversarial Attacks," arXiv preprint arXiv:1706.06083, 2017.
3 N. Carlini, D. Wagner, "Towards Evaluating the Robustness of Neural Networks," IEEE Symposium on Security and Privacy, 2017.
4 I. Goodfellow, et al., "Generative Adversarial Nets," Proceedings of the 27th International Conference on Neural Information Processing Systems, Vol. 2, pp. 2672-2680, 2014.
5 C. Szegedy, et al., "Intriguing Properties of Neural Networks," International Conference on Learning Representations, 2014.
6 S. Qiu, et al., "Review of Artificial Intelligence Adversarial Attack and Defense Technologies," Applied Sciences, 9(5), 2019.
7 X. Yuan, et al., "Adversarial Examples: Attacks and Defenses for Deep Learning," IEEE Transactions on Neural Networks and Learning Systems, 30(9), pp.2805-2824, Sep 2019.   DOI
8 S. Moosavi-Dezfooli, et al., "DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks," arXiv preprint arXiv:1511.04599, 2015.
9 X. Liu, et al., "DPatch: Attacking Object Detectors with Adversarial Patches," arXiv preprint arXiv:1806.02299, 2018.
10 C. Xiao, et al., "Generating Adversarial Examples with Adversarial Networks," arXiv preprint arXiv:1801.02610, 2018.
11 A. Liu, et al., "Perceptual-Sensitive GAN for Generating Adversarial Patches," Proceedings of the AAAI Conference on Artificial Intelligence, 33, pp. 1028-2035, 2019.
12 P. Isola, et al., "Image-to-Image Translation with Conditional Adversarial Networks," arXiv preprint arXiv:1611.07004, 2016.
13 J. Y. Zhu, et al., "Unpaired Image-to-Image Translation using Cycle-consistent Adversarial Networks," IEEE International Conference on Computer Vision (ICCV), pp. 2242-2251, 2017.
14 T. B. Brown, et al., "Adversarial Patch," arXiv preprint arXiv:1712.09665, 2018.
15 J. Stallkamp, et al., "Man vs. Computer: Benchmarking machine learning algorithms for traffic sign recognition," Neural Networks : the official journal of the International Neural Network Society, 32, 2012.
16 A. Krizhevsky, et al., "CIFAR-10 (Canadian Institute For Advanced Research)," http://www.cs.toronto.edu/-kriz/cifar.html.
17 K. Simonyan, A. Zisserman, "Very Deep Convolutional Networks for Large-scale Image Recognition," arXiv preprint arXiv:1409.1556, 2015.
18 K. He, "Deep Residual Learning for Image Recognition," arXiv preprint arXiv:1512.03385, 2015.
19 F. N. Iandola, et al., "SqueezeNet: AlexNet-level Accuracy with 50x Fewer Parameters and <0.5MB Model Size," arXiv preprint arXiv:1602.07360, 2016.
20 M. Sandler, et al., "MobileNetV2: Inverted Residuals and Linear Bottlenecks," arXiv preprint arXiv:1801.04381, 2019.
21 Z. Wang, et al., "Image Quality Assessment: From Error Visibility to Structural Similarity," IEEE Transactions on Image Processing, 13(4), pp. 600-612, 2004.   DOI