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In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance.
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Sequence to sequence learning with neural networks
I Sutskever, O Vinyals, and QV Le · 2014
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Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Tensorflow: A system for large-scale machine learning
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Deep speech 2: End-to-end speech recognition in english and mandarin
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Deep residual learning for image recognition
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Adversarial examples in the physical world, 2016
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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The limitations of deep learning in adversarial settings
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2017
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A rotation and a translation suffice: Fooling cnns with simple transformations
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2017
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Adversarial examples for semantic image segmentation, 2017
Volker Fischer, Mummadi Chaithanya Kumar, Jan Hendrik Metzen, and Thomas Brox · 2017
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Google’s cloud vision api is not robust to noise
Hossein Hosseini, Baicen Xiao, and Radha Poovendran · 2017
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Generate adversarial examples by spatially perturbing on the meaningful area
Ting Deng and Zhigang Zeng · 2019
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Transferability of adversarial examples to attack real world porn images detection service
Dou Goodman and Xin Hao · 2019
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Cloud-based image classification service is not robust to simple transformations: A forgotten battlefield, 2019
Dou Goodman and Tao Wei · 2019
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Cloud-based image classification service is not robust to affine transformation: A forgotten battlefield
Dou Goodman, Xin Hao, Yang Wang, Jiawei Tang, Yunhan Jia, Pei Wang, and Tao Wei · 2019
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Adversarial examples versus cloud-based detectors: A black-box empirical study
Xurong Li, Shouling Ji, Meng Han, Juntao Ji, Zhenyu Ren, Yushan Liu, and Chunming Wu · 2019
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Google’s cloud vision api is not robust to noise
Hossein Hosseini, Baicen Xiao, and Radha Poovendran · 2017
Cited alongside, same era.
Foolbox: A python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
Cited alongside, same era.
Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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Adversarial examples for semantic segmentation and object detection
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan Yuille · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2017
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Efficient defenses against adversarial attacks
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat · 2017
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Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow · 2018
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Deepsec: A uniform platform for security analysis of deep learning model
Xiang Ling, Shouling Ji, Jiaxu Zou, Jiannan Wang, Chunming Wu, Bo Li, and Ting Wang · 2019
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A tour of convolutional networks guided by linear interpreters
Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu, and Xingqun Jiang · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Imperceptible, robust, and targeted adversarial examples for automatic speech recognition, 2019
Yao Qin, Nicholas Carlini, Ian Goodfellow, Garrison Cottrell, and Colin Raffel · 2019
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Fooling automated surveillance cameras: adversarial patches to attack person detection, 2019
Simen Thys, Wiebe Van Ranst, and Toon Goedemé · 2019
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Evading real-time person detectors by adversarial t-shirt
Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, and Xue Lin · 2019
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Robust audio adversarial example for a physical attack
Hiromu Yakura and Jun Sakuma · 2019
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Stealthy porn: Understanding real-world adversarial images for illicit online promotion
Kan Yuan, Di Tang, Xiaojing Liao, XiaoFeng Wang, Xuan Feng, Yi Chen, Menghan Sun, Haoran Lu, and Kehuan Zhang · 2019
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Transferability of adversarial examples to attack cloud-based image classifier service, 2020
Dou Goodman · 2020
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Attacking and defending machine learning applications of public cloud
Dou Goodman and Xin Hao · 2020
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Fastwordbug: A fast method to generate adversarial text against nlp applications
Dou Goodman, Lv Zhonghou, et al · 2020
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Fooling detection alone is not enough: Adversarial attack against multiple object tracking
Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, and Hao Chen · 2020
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