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A deep neural network (DNN) classifier represents a model owner's intellectual property as training a DNN classifier often requires lots of resource.
The use of the area under the ROC curve in the evaluation of machine learning algorithms
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Learning both weights and connections for efficient neural network. In Advances in neural information processing systems
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ImageNet Large Scale Visual Recognition Challenge
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Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
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Stealing machine learning models via prediction apis. In USENIX Security Symposium
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. 2016 · 2016
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Wide Residual Networks. In British Machine Vision Conference 2016
Sergey Zagoruyko and Nikos Komodakis. 2016 · 2016
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Towards evaluating the robustness of neural networks. In IEEE Symposium on Security and Privacy
Nicholas Carlini and David Wagner. 2017 · 2017
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Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition
François Chollet. 2017 · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
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Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017 · 2017
Reverse engineering convolutional neural networks through side-channel information leaks. In ACM/ESDA/IEEE Design Automation Conference (DAC)
Weizhe Hua, Zhiru Zhang, and G Edward Suh. 2018 · 2018
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PRADA: protecting against DNN model stealing attacks
Mika Juuti, Sebastian Szyller, Alexey Dmitrenko, Samuel Marchal, and N Asokan. 2018 · 2018
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Digital watermarking for deep neural networks
Yuki Nagai, Yusuke Uchida, Shigeyuki Sakazawa, and Shin’ichi Satoh. 2018 · 2018
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Towards reverse-engineering black-box neural networks. In International Conference on Learning Representations
Seong Joon Oh, Max Augustin, Bernt Schiele, and Mario Fritz. 2018 · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2018 · 2018
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Cited alongside, same era.
Adversarial examples in the physical world. In International Conference on Learning Representations
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2017 · 2017
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Adversarial frontier stitching for remote neural network watermarking
Erwan Le Merrer, Patrick Perez, and Gilles Trédan. 2017 · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-First AAAI Conference on Artificial Intelligence
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi. 2017 · 2017
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Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. 2017 · 2017
Cited alongside, same era.
Turning your weakness into a strength: Watermarking deep neural networks by backdooring. In USENIX Security Symposium
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. 2018 · 2018
Cited alongside, same era.
Deepmarks: A digital fingerprinting framework for deep neural networks
Huili Chen, Bita Darvish Rohani, and Farinaz Koushanfar. 2018 · 2018
Cited alongside, same era.
Watermarking deep neural networks for embedded systems. In IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
Jia Guo and Miodrag Potkonjak. 2018 · 2018
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Stealing Hyperparameters in Machine Learning. In IEEE Symposium on Security and Privacy
Binghui Wang and Neil Zhenqiang Gong. 2018 · 2018
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Cache telepathy: Leveraging shared resource attacks to learn DNN architectures
Mengjia Yan, Christopher Fletcher, and Josep Torrellas. 2018 · 2018
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Protecting intellectual property of deep neural networks with watermarking. In Proceedings of the 2018 on Asia Conference on Computer and Communications Security
Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph Stoecklin, Heqing Huang, and Ian Molloy. 2018 · 2018
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Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. 2018 · 2018
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DeepSigns: An End-to-End Watermarking Framework for Ownership Protection of Deep Neural Networks. In Architectural Support for Programming Languages and Operating Systems
Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar. 2019 · 2019
Closest in time.
Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints. In arxiv
Xing Hu, Ling Liang, Lei Deng, Shuangchen Li, Xinfeng Xie, Yu Ji, Yufei Ding, Chang Liu, Timothy Sherwood, and Yuan Xie. 2019 · 2019
Closest in time.
How to prove your model belongs to you: a blind-watermark based framework to protect intellectual property of DNN. In Proceedings of the 35th Annual Computer Security Applications Conference
Zheng Li, Chengyu Hu, Yang Zhang, and Shanqing Guo. 2019 · 2019
Closest in time.