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Machine learning, with its myriad applications, has become an integral component of numerous technological systems.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew · 1910
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Adversarial model extraction on graph neural networks
David DeFazio and Arti Ramesh · 1912
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Model weight theft with just noise inputs: The curious case of the petulant attacker
Nicholas Roberts, Vinay Uday Prabhu, and Matthew McAteer · 1912
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Mlaas: Machine learning as a service
Mauro Ribeiro, Katarina Grolinger, and Miriam A. M. Capretz · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size
Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Copycat CNN: stealing knowledge by persuading confession with random non-labeled data
Jacson Rodrigues Correia da Silva, Rodrigo Ferreira Berriel, Claudine Badue, Alberto Ferreira de Souza, and Thiago Oliveira-Santos · 2018
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Stealing neural networks via timing side channels
Vasisht Duddu, Debasis Samanta, D. Vijay Rao, and Valentina Emilia Balas · 2018
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Security analysis of deep neural networks operating in the presence of cache side-channel attacks
Sanghyun Hong, Michael Davinroy, Yigitcan Kaya, Stuart Nevans Locke, Ian Rackow, Kevin Kulda, Dana Dachman-Soled, and Tudor Dumitras · 2018
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Reverse engineering convolutional neural networks through side-channel information leaks
Weizhe Hua, Zhiru Zhang, and G. Edward Suh · 2018
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Query-efficient black-box attack by active learning
Pengcheng Li, Jinfeng Yi, and Lijun Zhang · 2018
Cited alongside, same era.
Shufflenet V2: practical guidelines for efficient CNN architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
Cited alongside, same era.
Rendered insecure: GPU side channel attacks are practical
Hoda Naghibijouybari, Ajaya Neupane, Zhiyun Qian, and Nael B. Abu-Ghazaleh · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
I know what you see: Power side-channel attack on convolutional neural network accelerators
Lingxiao Wei, Bo Luo, Yu Li, Yannan Liu, and Qiang Xu · 2018
Cited alongside, same era.
CSI NN: reverse engineering of neural network architectures through electromagnetic side channel
Deepem: Deep neural networks model recovery through EM side-channel information leakage
Honggang Yu, Haocheng Ma, Kaichen Yang, Yiqiang Zhao, and Yier Jin · 2020
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On reverse engineering neural network implementation on GPU
Lukasz Chmielewski and Leo Weissbart · 2021
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Stealing links from graph neural networks
Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang · 2021
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MAZE: data-free model stealing attack using zeroth-order gradient estimation
Sanjay Kariyappa, Atul Prakash, and Moinuddin K. Qureshi · 2021
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Swin transformer V2: scaling up capacity and resolution
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, and Baining Guo · 2021
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Lejla Batina, Shivam Bhasin, Dirmanto Jap, and Stjepan Picek · 2019
Cited alongside, same era.
Stealing knowledge from protected deep neural networks using composite unlabeled data
Itay Mosafi, Eli (Omid) David, and Nathan S. Netanyahu · 2019
Cited alongside, same era.
Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
Cited alongside, same era.
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, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V. Le · 2019
Cited alongside, same era.
AFA: adversarial fingerprinting authentication for deep neural networks
Jingjing Zhao, Qingyue Hu, Gaoyang Liu, Xiaoqiang Ma, Fei Chen, and Mohammad Mehedi Hassan · 2019
Cited alongside, same era.
Deepsniffer: A DNN model extraction framework based on learning architectural hints
Xing Hu, Ling Liang, Shuangchen Li, Lei Deng, Pengfei Zuo, Yu Ji, Xinfeng Xie, Yufei Ding, Chang Liu, Timothy Sherwood, and Yuan Xie · 2020
Cited alongside, same era.
Adnan Siraj Rakin, Md Hafizul Islam Chowdhuryy, Fan Yao, and Deliang Fan · 2021
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Protecting artificial intelligence ips: a survey of watermarking and fingerprinting for machine learning
Francesco Regazzoni, Paolo Palmieri, Fethulah Smailbegovic, Rosario Cammarota, and Ilia Polian · 2021
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DAWN: dynamic adversarial watermarking of neural networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal, and N. Asokan · 2021
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Time to leak: Cross-device timing attack on edge deep learning accelerator
Yoo-Seung Won, Soham Chatterjee, Dirmanto Jap, Shivam Bhasin, and Arindam Basu · 2021
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Activeguard: An active DNN IP protection technique via adversarial examples
Mingfu Xue, Shichang Sun, Can He, Yushu Zhang, Jian Wang, and Weiqiang Liu · 2021
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Teacher model fingerprinting attacks against transfer learning
Yufei Chen, Chao Shen, Cong Wang, and Yang Zhang · 2022
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I know what you trained last summer: A survey on stealing machine learning models and defences
Daryna Oliynyk, Rudolf Mayer, and Andreas Rauber · 2022
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DNN model architecture fingerprinting attack on CPU-GPU edge devices
Kartik Patwari, Syed Mahbub Hafiz, Han Wang, Houman Homayoun, Zubair Shafiq, and Chen-Nee Chuah · 2022
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Model stealing attacks against inductive graph neural networks
Yun Shen, Xinlei He, Yufei Han, and Yang Zhang · 2022
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Model extraction attacks on graph neural networks: Taxonomy and realisation
Bang Wu, Xiangwen Yang, Shirui Pan, and Xingliang Yuan · 2022
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ES attack: Model stealing against deep neural networks without data hurdles
Xiaoyong Yuan, Leah Ding, Lan Zhang, Xiaolin Li, and Dapeng Oliver Wu · 2022
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Isabell Lederer, Rudolf Mayer, and Andreas Rauber · 2023
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On the feasibility of specialized ability stealing for large language code models
Zongjie Li, Chaozheng Wang, Pingchuan Ma, Chaowei Liu, Shuai Wang, Daoyuan Wu, and Cuiyun Gao · 2023
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Ezclone: Improving DNN model extraction attack via shape distillation from GPU execution profiles
Jonah O’Brien Weiss, Tiago A. O. Alves, and Sandip Kundu · 2023
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