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Many recent works demonstrated that Deep Learning models are vulnerable to adversarial examples.Fortunately, generating adversarial examples usually requires white-box access to the victim model, and the attacker can only access the APIs opened by cloud platforms.
RELIABLE AND FAST STRUCTURE-ORIENTED VIDEO NOISE ESTIMATION
Aishy Amer and Eric Dubois · 2002
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, Eero P Simoncelli, et al · 2004
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Fast and reliable structure-oriented video noise estimation
Aishy Amer and Eric Dubois · 2005
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Sun Jian · 2015
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng Yang Fu, and Alexander C. Berg · 2016
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Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Liu Chang, and Dawn Song · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
Adversarial patch
Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
Cited alongside, same era.
A rotation and a translation suffice: Fooling cnns with simple transformations
Query-efficient black-box adversarial examples (superceded)
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Later among the works it cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, and Vitaly Shmatikov · 2017
Later among the works it cites.
Spatially transformed adversarial examples
Chaowei Xiao, Jun Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song · 2018
Later among the works it cites.
Transferability of adversarial examples to attack real world porn images detection service
Dou Goodman and Xin Hao · 2019
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Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2017
Cited alongside, same era.
Machine learning as an adversarial service: Learning black-box adversarial examples
Jamie Hayes and George Danezis · 2017
Cited alongside, same era.
Google’s cloud vision api is not robust to noise
Hossein Hosseini, Baicen Xiao, and Radha Poovendran · 2017
Cited alongside, same era.
Stealthy porn: Understanding real-world adversarial images for illicit online promotion
Kan Yuan, Di Tang, Xiaojing Liao, Xiao Feng Wang, Xuan Feng, Yi Chen, Menghan Sun, Haoran Lu, and Kehuan Zhang
Cited in the paper.
Transferability of adversarial examples to attack cloud-based image classifier service
Dou Goodman, Xin Hao, and Yang Wang · 2019
Closest in time.
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, and Chunming Wu · 2019
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