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Device-bind key-storageless hardware AI model IP protection: a PUF and permute-diffusion encryption-enabled approach. arXiv:2212.11133. 2022."},{"key":"ref21","doi-asserted-by":"crossref","first-page":"6122","DOI":"10.1109\/TPAMI.2021.3088846","article-title":"DeepIPR: deep neural network ownership verification with passports","volume":"44","author":"Fan","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref22","series-title":"2020 57th ACM\/IEEE Design Automation Conference (DAC)","first-page":"1","article-title":"Hardware-assisted intellectual property protection of deep learning models","author":"Chakraborty","year":"2020 Jul 20\u201324"},{"key":"ref23","series-title":"ICML\u201923: International Conference on Machine Learning","first-page":"42614","article-title":"NNSplitter: an active defense solution for DNN model via automated weight obfuscation","author":"Zhou","year":"2023 Jul 23\u201329"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s11071-017-3436-y","article-title":"An image encryption algorithm based on compound homogeneous hyper-chaotic system","volume":"89","author":"Zhu","year":"2017","journal-title":"Nonlinear Dyn"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1007\/s11071-018-4426-4","article-title":"A novel plaintext-related image encryption scheme using hyper-chaotic system","volume":"94","author":"Li","year":"2018","journal-title":"Nonlinear Dyn"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/TCAD.2020.3018403","article-title":"Chaotic weights: a novel approach to protect intellectual property of deep neural networks","volume":"40","author":"Lin","year":"2020","journal-title":"IEEE Trans Comput-Aided Des Integr Circuits Syst"},{"key":"ref27","first-page":"3146","article-title":"Protecting the intellectual property of binary deep neural networks with efficient spintronic-based hardware obfuscation","volume":"71","author":"Mohseni","year":"2024","journal-title":"IEEE Trans Circuits Syst I: Regul Pap"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3679202","article-title":"SSAT: active authorization control and user\u2019s fingerprint tracking framework for DNN IP protection","volume":"20","author":"Xue","year":"2024","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"106199","DOI":"10.1016\/j.neunet.2024.106199","article-title":"Proactive intellectual property protection and model security defense for DNNs based on backdoor learning","volume":"174","author":"Li","year":"2024","journal-title":"Neural Netw"},{"key":"ref30","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009","journal-title":"Technical Report"},{"key":"ref31","unstructured":"Xiao H, Rasul K, Vollgraf R. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747. 2017."},{"key":"ref32","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"ImageNet: a large-scale hierarchical image database","author":"Deng","year":"2009 Jun 20\u201325"},{"key":"ref33","series-title":"International Conference on Machine Learning","first-page":"6105","article-title":"EfficientNet: rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019 Jun 9\u201315"},{"key":"ref34","series-title":"Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2018 Jun 18\u201323; Salt Lake City, UT, USA","article-title":"MobileNetV2: inverted residuals and linear bottlenecks","author":"Sandler"},{"key":"ref35","series-title":"Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27\u201330; Las Vegas, NV, USA","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He"},{"key":"ref36","series-title":"Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition; 2018 Jun 18\u201323; Salt Lake City, UT, USA","first-page":"6848","article-title":"ShuffleNet: an extremely efficient convolutional neural network for mobile devices","author":"Zhang"},{"key":"ref37","unstructured":"Simonyan K, Zisserman A. 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