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Did you hear that? Adversarial Examples Against Automatic Speech Recognition. arXiv:1801.00554 (2018).  Moustafa Alzantot and et al. 2018. Did you hear that? Adversarial Examples Against Automatic Speech Recognition. arXiv:1801.00554 (2018).","key":"e_1_3_2_1_1_1"},{"doi-asserted-by":"crossref","unstructured":"Nicholas Carlini and etal 2018. Audio Adversarial Examples: Targeted Attacks on Speech-to-Text. arXiv:1801.01944 (2018).  Nicholas Carlini and et al. 2018. 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Google Voice. https:\/\/voice.google.com\/u\/0\/about.","key":"e_1_3_2_1_5_1"},{"unstructured":"Awni Hannun and etal 2014. Deep speech: Scaling up end-to-end speech recognition. arXiv preprint arXiv 1412.5567 (2014).  Awni Hannun and et al. 2014. Deep speech: Scaling up end-to-end speech recognition. arXiv preprint arXiv 1412.5567 (2014).","key":"e_1_3_2_1_6_1"},{"unstructured":"Imagga. 2017. The Top 5 Uses of Image Recognition. https:\/\/imagga.com\/blog\/the-top-5-uses-of-image-recognition\/  Imagga. 2017. The Top 5 Uses of Image Recognition. https:\/\/imagga.com\/blog\/the-top-5-uses-of-image-recognition\/","key":"e_1_3_2_1_7_1"},{"key":"e_1_3_2_1_8_1","first-page":"137","article-title":"Method and apparatus for enabling transmission of data packets over a bypass circuit-switched public telephone connection","volume":"6","author":"Jonas Howard","year":"2000","unstructured":"Howard Jonas and 2000 . Method and apparatus for enabling transmission of data packets over a bypass circuit-switched public telephone connection . US Patent 6 , 137 ,792. Howard Jonas and et al. 2000. Method and apparatus for enabling transmission of data packets over a bypass circuit-switched public telephone connection. US Patent 6,137,792.","journal-title":"US Patent"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.4236\/jcc.2015.36001"},{"unstructured":"Alex Krizhevsky and etal 2009. Learning multiple layers of features from tiny images. Technical Report. Citeseer.  Alex Krizhevsky and et al. 2009. Learning multiple layers of features from tiny images. Technical Report. Citeseer.","key":"e_1_3_2_1_10_1"},{"unstructured":"Alex Krizhevsky and etal 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.   Alex Krizhevsky and et al. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.","key":"e_1_3_2_1_11_1"},{"unstructured":"Alexey Kurakin and etal 2016. Adversarial examples in the physical world. arXiv:1607.02533 (2016).  Alexey Kurakin and et al. 2016. Adversarial examples in the physical world. arXiv:1607.02533 (2016).","key":"e_1_3_2_1_12_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1109\/5.726791"},{"unstructured":"Newman Lily. 2016. Hackers Trick Facial-Recognition Logins With Photos From Facebook (What Else?). https:\/\/www.wired.com\/2016\/08\/hackers-trick-facial-recognition-logins-photos-facebook-thanks-zuck\/  Newman Lily. 2016. 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What You Need to Know About Voice Hacking. https:\/\/productivitybytes.com\/need-know-voice-hacking\/  Kayla Matthews. 2016. What You Need to Know About Voice Hacking. https:\/\/productivitybytes.com\/need-know-voice-hacking\/","key":"e_1_3_2_1_16_1"},{"unstructured":"Microsoft. 2018. Bing Voice. https:\/\/azure.microsoft.com\/en-us\/services\/cognitive-services\/speech\/.  Microsoft. 2018. Bing Voice. https:\/\/azure.microsoft.com\/en-us\/services\/cognitive-services\/speech\/.","key":"e_1_3_2_1_17_1"},{"doi-asserted-by":"crossref","unstructured":"Seyed-Mohsen Moosavi-Dezfooli and etal 2017. Universal adversarial perturbations. arXiv preprint (2017).  Seyed-Mohsen Moosavi-Dezfooli and et al. 2017. Universal adversarial perturbations. arXiv preprint (2017).","key":"e_1_3_2_1_18_1","DOI":"10.1109\/CVPR.2017.17"},{"unstructured":"Mozilla. 2017. Common Voice. https:\/\/voice.mozilla.org\/en.  Mozilla. 2017. 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