Perceptual Hash of Neural Networks

In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the c...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhiying Zhu [verfasserIn] Hang Zhou [verfasserIn] Siyuan Xing [verfasserIn] Zhenxing Qian [verfasserIn] Sheng Li [verfasserIn] Xinpeng Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	perceptual hash DNN model retrieval graph hash HNN-Net

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 14(2022), 4, p 810
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:4, p 810

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/sym14040810

Katalog-ID:	DOAJ085341703

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allfields	10.3390/sym14040810 doi (DE-627)DOAJ085341703 (DE-599)DOAJ053d0b1447f84eeb8f9defe75a6f4bc9 DE-627 ger DE-627 rakwb eng QA1-939 Zhiying Zhu verfasserin aut Perceptual Hash of Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models. perceptual hash DNN model retrieval graph hash HNN-Net Mathematics Hang Zhou verfasserin aut Siyuan Xing verfasserin aut Zhenxing Qian verfasserin aut Sheng Li verfasserin aut Xinpeng Zhang verfasserin aut In Symmetry MDPI AG, 2009 14(2022), 4, p 810 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:14 year:2022 number:4, p 810 https://doi.org/10.3390/sym14040810 kostenfrei https://doaj.org/article/053d0b1447f84eeb8f9defe75a6f4bc9 kostenfrei https://www.mdpi.com/2073-8994/14/4/810 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 4, p 810
spelling	10.3390/sym14040810 doi (DE-627)DOAJ085341703 (DE-599)DOAJ053d0b1447f84eeb8f9defe75a6f4bc9 DE-627 ger DE-627 rakwb eng QA1-939 Zhiying Zhu verfasserin aut Perceptual Hash of Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models. perceptual hash DNN model retrieval graph hash HNN-Net Mathematics Hang Zhou verfasserin aut Siyuan Xing verfasserin aut Zhenxing Qian verfasserin aut Sheng Li verfasserin aut Xinpeng Zhang verfasserin aut In Symmetry MDPI AG, 2009 14(2022), 4, p 810 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:14 year:2022 number:4, p 810 https://doi.org/10.3390/sym14040810 kostenfrei https://doaj.org/article/053d0b1447f84eeb8f9defe75a6f4bc9 kostenfrei https://www.mdpi.com/2073-8994/14/4/810 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 4, p 810
allfields_unstemmed	10.3390/sym14040810 doi (DE-627)DOAJ085341703 (DE-599)DOAJ053d0b1447f84eeb8f9defe75a6f4bc9 DE-627 ger DE-627 rakwb eng QA1-939 Zhiying Zhu verfasserin aut Perceptual Hash of Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models. perceptual hash DNN model retrieval graph hash HNN-Net Mathematics Hang Zhou verfasserin aut Siyuan Xing verfasserin aut Zhenxing Qian verfasserin aut Sheng Li verfasserin aut Xinpeng Zhang verfasserin aut In Symmetry MDPI AG, 2009 14(2022), 4, p 810 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:14 year:2022 number:4, p 810 https://doi.org/10.3390/sym14040810 kostenfrei https://doaj.org/article/053d0b1447f84eeb8f9defe75a6f4bc9 kostenfrei https://www.mdpi.com/2073-8994/14/4/810 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 4, p 810
allfieldsGer	10.3390/sym14040810 doi (DE-627)DOAJ085341703 (DE-599)DOAJ053d0b1447f84eeb8f9defe75a6f4bc9 DE-627 ger DE-627 rakwb eng QA1-939 Zhiying Zhu verfasserin aut Perceptual Hash of Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models. perceptual hash DNN model retrieval graph hash HNN-Net Mathematics Hang Zhou verfasserin aut Siyuan Xing verfasserin aut Zhenxing Qian verfasserin aut Sheng Li verfasserin aut Xinpeng Zhang verfasserin aut In Symmetry MDPI AG, 2009 14(2022), 4, p 810 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:14 year:2022 number:4, p 810 https://doi.org/10.3390/sym14040810 kostenfrei https://doaj.org/article/053d0b1447f84eeb8f9defe75a6f4bc9 kostenfrei https://www.mdpi.com/2073-8994/14/4/810 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 4, p 810
allfieldsSound	10.3390/sym14040810 doi (DE-627)DOAJ085341703 (DE-599)DOAJ053d0b1447f84eeb8f9defe75a6f4bc9 DE-627 ger DE-627 rakwb eng QA1-939 Zhiying Zhu verfasserin aut Perceptual Hash of Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models. perceptual hash DNN model retrieval graph hash HNN-Net Mathematics Hang Zhou verfasserin aut Siyuan Xing verfasserin aut Zhenxing Qian verfasserin aut Sheng Li verfasserin aut Xinpeng Zhang verfasserin aut In Symmetry MDPI AG, 2009 14(2022), 4, p 810 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:14 year:2022 number:4, p 810 https://doi.org/10.3390/sym14040810 kostenfrei https://doaj.org/article/053d0b1447f84eeb8f9defe75a6f4bc9 kostenfrei https://www.mdpi.com/2073-8994/14/4/810 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 4, p 810
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authorswithroles_txt_mv	Zhiying Zhu @@aut@@ Hang Zhou @@aut@@ Siyuan Xing @@aut@@ Zhenxing Qian @@aut@@ Sheng Li @@aut@@ Xinpeng Zhang @@aut@@
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callnumber-first	Q - Science
author	Zhiying Zhu
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topic_title	QA1-939 Perceptual Hash of Neural Networks perceptual hash DNN model retrieval graph hash HNN-Net
topic	misc QA1-939 misc perceptual hash misc DNN misc model retrieval misc graph hash misc HNN-Net misc Mathematics
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title	Perceptual Hash of Neural Networks
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title_auth	Perceptual Hash of Neural Networks
abstract	In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models.
abstractGer	In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models.
abstract_unstemmed	In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models.
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title_short	Perceptual Hash of Neural Networks
url	https://doi.org/10.3390/sym14040810 https://doaj.org/article/053d0b1447f84eeb8f9defe75a6f4bc9 https://www.mdpi.com/2073-8994/14/4/810 https://doaj.org/toc/2073-8994
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