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Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Brian Kingsbury, et al. Deep neural networks for acoustic modeling in speech recognition. IEEE Signal processing magazine, 29, 2012."},{"key":"e_1_3_2_1_5_1","volume-title":"Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882","author":"Kim Yoon","year":"2014","unstructured":"Yoon Kim . Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 , 2014 . Yoon Kim. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882, 2014."},{"key":"e_1_3_2_1_6_1","volume-title":"Bert: Pretraining of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . Bert: Pretraining of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 , 2018 . Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pretraining of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018."},{"key":"e_1_3_2_1_7_1","volume-title":"Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199","author":"Szegedy Christian","year":"2013","unstructured":"Christian Szegedy , Wojciech Zaremba , Ilya Sutskever , Joan Bruna , Dumitru Erhan , Ian Goodfellow , and Rob Fergus . Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 , 2013 . Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013."},{"key":"e_1_3_2_1_8_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow , Jonathon Shlens , and Christian Szegedy . Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 , 2014 . Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014."},{"key":"e_1_3_2_1_9_1","volume-title":"Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533","author":"Kurakin Alexey","year":"2016","unstructured":"Alexey Kurakin , Ian Goodfellow , and Samy Bengio . Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 , 2016 . Alexey Kurakin, Ian Goodfellow, and Samy Bengio. Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533, 2016."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.17"},{"key":"e_1_3_2_1_15_1","volume-title":"Improving black-box adversarial attacks with a transfer-based prior. arXiv preprint arXiv:1906.06919","author":"Cheng Shuyu","year":"2019","unstructured":"Shuyu Cheng , Yinpeng Dong , Tianyu Pang , Hang Su , and Jun Zhu . Improving black-box adversarial attacks with a transfer-based prior. arXiv preprint arXiv:1906.06919 , 2019 . Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Improving black-box adversarial attacks with a transfer-based prior. arXiv preprint arXiv:1906.06919, 2019."},{"key":"e_1_3_2_1_16_1","volume-title":"Badnets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733","author":"Gu Tianyu","year":"2017","unstructured":"Tianyu Gu , Brendan Dolan-Gavitt , and Siddharth Garg . Badnets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733 , 2017 . Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. Badnets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733, 2017."},{"key":"e_1_3_2_1_17_1","volume-title":"ios-siri-apple. https:\/\/www.apple.com\/ios\/siri\/","year":"2016","unstructured":"Apple. ios-siri-apple. https:\/\/www.apple.com\/ios\/siri\/ , 2016 . Apple. ios-siri-apple. https:\/\/www.apple.com\/ios\/siri\/, 2016."},{"key":"e_1_3_2_1_18_1","volume-title":"Cortana-your intelligent virtual and personal assistant - microsoft. https:\/\/www.microsoft.com\/en-us\/windows\/cortana","year":"2016","unstructured":"Microsoft. Cortana-your intelligent virtual and personal assistant - microsoft. https:\/\/www.microsoft.com\/en-us\/windows\/cortana , 2016 . Microsoft. Cortana-your intelligent virtual and personal assistant - microsoft. https:\/\/www.microsoft.com\/en-us\/windows\/cortana, 2016."},{"key":"e_1_3_2_1_19_1","volume-title":"Feature squeezing: Detecting adversarial examples in deep neural networks. network and distributed system security symposium","author":"Xu Weilin","year":"2018","unstructured":"Weilin Xu , David Evans , and Yanjun Qi . Feature squeezing: Detecting adversarial examples in deep neural networks. network and distributed system security symposium , 2018 . Weilin Xu, David Evans, and Yanjun Qi. Feature squeezing: Detecting adversarial examples in deep neural networks. network and distributed system security symposium, 2018."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134057"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.56"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2016.2616357"},{"key":"e_1_3_2_1_24_1","volume-title":"Neural network model extraction attacks in edge devices by hearing architectural hints. arXiv preprint arXiv:1903.03916","author":"Hu Xing","year":"2019","unstructured":"Xing Hu , Ling Liang , Lei Deng , Shuangchen Li , Xinfeng Xie , Yu Ji , Yufei Ding , Chang Liu , Timothy Sherwood , and Yuan Xie . Neural network model extraction attacks in edge devices by hearing architectural hints. arXiv preprint arXiv:1903.03916 , 2019 . Xing Hu, Ling Liang, Lei Deng, Shuangchen Li, Xinfeng Xie, Yu Ji, Yufei Ding, Chang Liu, Timothy Sherwood, and Yuan Xie. Neural network model extraction attacks in edge devices by hearing architectural hints. arXiv preprint arXiv:1903.03916, 2019."},{"key":"e_1_3_2_1_25_1","volume-title":"A3t: Adversarially augmented adversarial training. arXiv preprint arXiv:1801.04055","author":"Erraqabi Akram","year":"2018","unstructured":"Akram Erraqabi , Aristide Baratin , Yoshua Bengio , and Simon Lacoste- Julien . A3t: Adversarially augmented adversarial training. arXiv preprint arXiv:1801.04055 , 2018 . Akram Erraqabi, Aristide Baratin, Yoshua Bengio, and Simon Lacoste- Julien. A3t: Adversarially augmented adversarial training. arXiv preprint arXiv:1801.04055, 2018."},{"key":"e_1_3_2_1_26_1","volume-title":"On detecting adversarial perturbations. arXiv preprint arXiv:1702.04267","author":"Metzen Jan Hendrik","year":"2017","unstructured":"Jan Hendrik Metzen , Tim Genewein , Volker Fischer , and Bastian Bischoff . On detecting adversarial perturbations. arXiv preprint arXiv:1702.04267 , 2017 . Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff. On detecting adversarial perturbations. arXiv preprint arXiv:1702.04267, 2017."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.615"},{"key":"e_1_3_2_1_28_1","volume-title":"International Conference on Learning Representations","author":"Raghunathan Aditi","year":"2018","unstructured":"Aditi Raghunathan , Jacob Steinhardt , and Percy Liang . Certified defenses against adversarial examples . International Conference on Learning Representations , 2018 . Aditi Raghunathan, Jacob Steinhardt, and Percy Liang. Certified defenses against adversarial examples. International Conference on Learning Representations, 2018."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00191"},{"key":"e_1_3_2_1_30_1","volume-title":"Adversarial feature genome: a data driven adversarial examples recognition method. arXiv preprint arXiv:1812.10085","author":"Chen Li","year":"2018","unstructured":"Li Chen , Hailun Ding , Qi Li , Jiawei Zhu , Haozhe Huang , Yifan Chang , and Haifeng Li . Adversarial feature genome: a data driven adversarial examples recognition method. arXiv preprint arXiv:1812.10085 , 2018 . Li Chen, Hailun Ding, Qi Li, Jiawei Zhu, Haozhe Huang, Yifan Chang, and Haifeng Li. Adversarial feature genome: a data driven adversarial examples recognition method. arXiv preprint arXiv:1812.10085, 2018."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2019.23415"},{"key":"e_1_3_2_1_32_1","volume-title":"Hashtran-dnn: A framework for enhancing robustness of deep neural networks against adversarial malware samples. arXiv preprint arXiv:1809.06498","author":"Li Deqiang","year":"2018","unstructured":"Deqiang Li , Ramesh Baral , Tao Li , Han Wang , Qianmu Li , and Shouhuai Xu . Hashtran-dnn: A framework for enhancing robustness of deep neural networks against adversarial malware samples. arXiv preprint arXiv:1809.06498 , 2018 . Deqiang Li, Ramesh Baral, Tao Li, Han Wang, Qianmu Li, and Shouhuai Xu. 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Proceedings of the 35th International Conference on Machine Learning, 2018."},{"key":"e_1_3_2_1_35_1","volume-title":"Detecting adversarial samples from artifacts. arXiv preprint arXiv:1703.00410","author":"Feinman Reuben","year":"2017","unstructured":"Reuben Feinman , Ryan R Curtin , Saurabh Shintre , and Andrew B Gardner . Detecting adversarial samples from artifacts. arXiv preprint arXiv:1703.00410 , 2017 . Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner. Detecting adversarial samples from artifacts. arXiv preprint arXiv:1703.00410, 2017."},{"key":"e_1_3_2_1_36_1","volume-title":"please! adversarial defense via attention rectification and preservation. arXiv preprint arXiv:1811.09831","author":"Wu Shangxi","year":"2018","unstructured":"Shangxi Wu , Jitao Sang , Kaiyuan Xu , Jiaming Zhang , Yanfeng Sun , Liping Jing , and Jian Yu. Attention , please! adversarial defense via attention rectification and preservation. arXiv preprint arXiv:1811.09831 , 2018 . Shangxi Wu, Jitao Sang, Kaiyuan Xu, Jiaming Zhang, Yanfeng Sun, Liping Jing, and Jian Yu. Attention, please! adversarial defense via attention rectification and preservation. arXiv preprint arXiv:1811.09831, 2018."},{"key":"e_1_3_2_1_37_1","volume-title":"International Conference on Learning Representations","author":"Song Yang","year":"2018","unstructured":"Yang Song , Taesup Kim , Sebastian Nowozin , Stefano Ermon , and Nate Kushman . Pixeldefend : Leveraging generative models to understand and defend against adversarial examples . International Conference on Learning Representations , 2018 . Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman. Pixeldefend: Leveraging generative models to understand and defend against adversarial examples. International Conference on Learning Representations, 2018."},{"key":"e_1_3_2_1_38_1","first-page":"7717","volume-title":"Xiangyu Zhang. Attacks meet interpretability: Attribute-steered detection of adversarial samples. In Advances in Neural Information Processing Systems","author":"Tao Guanhong","year":"2018","unstructured":"Guanhong Tao , Shiqing Ma , Yingqi Liu , and Xiangyu Zhang. Attacks meet interpretability: Attribute-steered detection of adversarial samples. In Advances in Neural Information Processing Systems , pages 7717 -- 7728 , 2018 . Guanhong Tao, Shiqing Ma, Yingqi Liu, and Xiangyu Zhang. Attacks meet interpretability: Attribute-steered detection of adversarial samples. In Advances in Neural Information Processing Systems, pages 7717--7728, 2018."},{"key":"e_1_3_2_1_39_1","volume-title":"Alexander G de G Matthews, and Zoubin Ghahramani. Adversarial examples, uncertainty, and transfer testing robustness in gaussian process hybrid deep networks. arXiv preprint arXiv:1707.02476","author":"Bradshaw John","year":"2017","unstructured":"John Bradshaw , Alexander G de G Matthews, and Zoubin Ghahramani. Adversarial examples, uncertainty, and transfer testing robustness in gaussian process hybrid deep networks. arXiv preprint arXiv:1707.02476 , 2017 . John Bradshaw, Alexander G de G Matthews, and Zoubin Ghahramani. Adversarial examples, uncertainty, and transfer testing robustness in gaussian process hybrid deep networks. arXiv preprint arXiv:1707.02476, 2017."},{"key":"e_1_3_2_1_40_1","volume-title":"Puvae: A variational autoencoder to purify adversarial examples. arXiv preprint arXiv:1903.00585","author":"Hwang Uiwon","year":"2019","unstructured":"Uiwon Hwang , Jaewoo Park , Hyemi Jang , Sungroh Yoon , and Nam Ik Cho . Puvae: A variational autoencoder to purify adversarial examples. arXiv preprint arXiv:1903.00585 , 2019 . Uiwon Hwang, Jaewoo Park, Hyemi Jang, Sungroh Yoon, and Nam Ik Cho. Puvae: A variational autoencoder to purify adversarial examples. arXiv preprint arXiv:1903.00585, 2019."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00624"},{"key":"e_1_3_2_1_42_1","volume-title":"On the (statistical) detection of adversarial examples. arXiv preprint arXiv:1702.06280","author":"Grosse Kathrin","year":"2017","unstructured":"Kathrin Grosse , Praveen Manoharan , Nicolas Papernot , Michael Backes , and Patrick McDaniel . On the (statistical) detection of adversarial examples. arXiv preprint arXiv:1702.06280 , 2017 . Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel. On the (statistical) detection of adversarial examples. arXiv preprint arXiv:1702.06280, 2017."},{"key":"e_1_3_2_1_43_1","volume-title":"Detecting adversarial samples from artifacts. arXiv preprint arXiv:1703.00410","author":"Feinman Reuben","year":"2017","unstructured":"Reuben Feinman , Ryan R Curtin , Saurabh Shintre , and Andrew B Gardner . Detecting adversarial samples from artifacts. arXiv preprint arXiv:1703.00410 , 2017 . Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner. Detecting adversarial samples from artifacts. arXiv preprint arXiv:1703.00410, 2017."},{"key":"e_1_3_2_1_44_1","volume-title":"A computationally efficient method for defending adversarial deep learning attacks. arXiv preprint arXiv:1906.05599","author":"Sahay Rajeev","year":"2019","unstructured":"Rajeev Sahay , Rehana Mahfuz , and Aly El Gamal . A computationally efficient method for defending adversarial deep learning attacks. arXiv preprint arXiv:1906.05599 , 2019 . Rajeev Sahay, Rehana Mahfuz, and Aly El Gamal. A computationally efficient method for defending adversarial deep learning attacks. arXiv preprint arXiv:1906.05599, 2019."},{"key":"e_1_3_2_1_45_1","volume-title":"Defending against adversarial examples with k-nearest neighbor. arXiv preprint arXiv:1906.09525","author":"Sitawarin Chawin","year":"2019","unstructured":"Chawin Sitawarin and David Wagner . Defending against adversarial examples with k-nearest neighbor. arXiv preprint arXiv:1906.09525 , 2019 . Chawin Sitawarin and David Wagner. Defending against adversarial examples with k-nearest neighbor. arXiv preprint arXiv:1906.09525, 2019."},{"key":"e_1_3_2_1_46_1","volume-title":"Adversarial robustness via adversarial label-smoothing. arXiv preprint arXiv:1906.11567","author":"Goibert Morgane","year":"2019","unstructured":"Morgane Goibert and Elvis Dohmatob . Adversarial robustness via adversarial label-smoothing. arXiv preprint arXiv:1906.11567 , 2019 . Morgane Goibert and Elvis Dohmatob. Adversarial robustness via adversarial label-smoothing. arXiv preprint arXiv:1906.11567, 2019."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00019"},{"key":"e_1_3_2_1_48_1","volume-title":"Image super-resolution as a defense against adversarial attacks. arXiv preprint arXiv:1901.01677","author":"Mustafa Aamir","year":"2019","unstructured":"Aamir Mustafa , Salman H Khan , Munawar Hayat , Jianbing Shen , and Ling Shao . Image super-resolution as a defense against adversarial attacks. arXiv preprint arXiv:1901.01677 , 2019 . Aamir Mustafa, Salman H Khan, Munawar Hayat, Jianbing Shen, and Ling Shao. Image super-resolution as a defense against adversarial attacks. arXiv preprint arXiv:1901.01677, 2019."},{"key":"e_1_3_2_1_49_1","volume-title":"Teck Khim Ng, and Ee-Chien Chang. 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Detecting adversarial examples and other misclassifications in neural networks by introspection. arXiv preprint arXiv:1905.09186 , 2019 . Jonathan Aigrain and Marcin Detyniecki. Detecting adversarial examples and other misclassifications in neural networks by introspection. arXiv preprint arXiv:1905.09186, 2019."},{"key":"e_1_3_2_1_54_1","volume-title":"Detecting and diagnosing adversarial images with class-conditional capsule reconstructions. arXiv preprint arXiv:1907.02957","author":"Qin Yao","year":"2019","unstructured":"Yao Qin , Nicholas Frosst , Sara Sabour , Colin Raffel , Garrison Cottrell , and Geoffrey Hinton . Detecting and diagnosing adversarial images with class-conditional capsule reconstructions. arXiv preprint arXiv:1907.02957 , 2019 . Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison Cottrell, and Geoffrey Hinton. 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Puvae: A variational autoencoder to purify adversarial examples. arXiv preprint arXiv:1903.00585, 2019."},{"key":"e_1_3_2_1_57_1","volume-title":"Improving the robustness of deep neural networks via adversarial training with triplet loss. arXiv preprint arXiv:1905.11713","author":"Li Pengcheng","year":"2019","unstructured":"Pengcheng Li , Jinfeng Yi , Bowen Zhou , and Lijun Zhang . Improving the robustness of deep neural networks via adversarial training with triplet loss. arXiv preprint arXiv:1905.11713 , 2019 . Pengcheng Li, Jinfeng Yi, Bowen Zhou, and Lijun Zhang. Improving the robustness of deep neural networks via adversarial training with triplet loss. arXiv preprint arXiv:1905.11713, 2019."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00068"},{"key":"e_1_3_2_1_59_1","volume-title":"Defense against adversarial attacks using feature scattering-based adversarial training. arXiv preprint arXiv:1907.10764","author":"Zhang Haichao","year":"2019","unstructured":"Haichao Zhang and Jianyu Wang . Defense against adversarial attacks using feature scattering-based adversarial training. arXiv preprint arXiv:1907.10764 , 2019 . Haichao Zhang and Jianyu Wang. 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