research-article

Convolutional Neural Network Protection Method of Lenet-5-Like Structure

Authors:

Lei Sun,

Yuehan Wang,

Leyu DaiAuthors Info & Claims

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

Pages 77 - 80

https://doi.org/10.1145/3297156.3297224

Published: 08 December 2018 Publication History

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Abstract

In this paper, we build and describe a convolutional neural network protection method of Lenet-5-like structure. The protected convolutional neural network is obtained by adding the protection lock module in forward propagation algorithm in different layers so that unauthorized user can not use protected convolutional neural network of Lenet-5-like structure. The experimental results show that protection lock module be added in first and middle layers would gain the best protection effect. Authorized users can use protected convolutional neural network of Lenet-5-like structure after adding unlocking module. The accuracy of unlocked convolutional neural network is not affected by adding protection lock module and unlocking module. To add protection lock module realize protection on the convolutional neural network of Lenet-5-like structure.

References

[1]

Warren S. McCulloch, Walter Pitts. A logical calculus of the ideas immanent in nervous activity{J}, 1943, 5(4):115--133.

Google Scholar

[2]

Fukushima K, Miyake S, Ito T. Neocognitron: A neural network model for a mechanism of visual pattern recognition.{J}. Systems Man & Cybernetics IEEE Transactions on, 1983, SMC-13(5):826--834.Sherrington C S. Observations on the Scratch Reflex in the Spinal Dog.{J}. Journal of Physiology, 1906, 34(1--2):1.

Crossref

Google Scholar

[3]

Orlandi C, Piva A, Barni M. Oblivious Neural Network Computing via Homomorphic Encryption{J}. Eurasip Journal on Information Security, 2007, 2007(1):1--11.

Digital Library

Google Scholar

[4]

Bos J W, Lauter K, Loftus J, et al. Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme{M}// Cryptography and Coding. Springer Berlin Heidelberg, 2013:45--64.

Digital Library

Google Scholar

[5]

Dowlin N, Ran G B, Laine K, et al. CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy{C}// Radio and Wireless Symposium. IEEE, 2016:76--78.

Digital Library

Google Scholar

[6]

Lécun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition{J}. Proceedings of the IEEE, 1998, 86(11):2278--2324.

Crossref

Google Scholar

[7]

Goodfellow I, Bengio Y, Courville A. Deep Learning{M}. The MIT Press, 2016:15.

Digital Library

Google Scholar

Cited By

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Lederer IMayer RRauber A(2024)Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and AttacksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.327013535:10(13082-13100)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3270135
Rossolini GBiondi AButtazzo G(2023)Increasing the Confidence of Deep Neural Networks by Coverage AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2022.316368249:2(802-815)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TSE.2022.3163682
Booth JBolet G(2023)Neural Acceleration of Graph Based Utility Functions for Sparse MatricesIEEE Access10.1109/ACCESS.2023.326245311(31619-31635)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3262453

Index Terms

Convolutional Neural Network Protection Method of Lenet-5-Like Structure
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 2018

641 pages

ISBN:9781450366069

DOI:10.1145/3297156

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 December 2018

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The National Key Research and Development Program of China

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CSAI '18

CSAI '18: 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 8 - 10, 2018

Shenzhen, China

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Cited By

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Lederer IMayer RRauber A(2024)Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and AttacksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.327013535:10(13082-13100)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3270135
Rossolini GBiondi AButtazzo G(2023)Increasing the Confidence of Deep Neural Networks by Coverage AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2022.316368249:2(802-815)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TSE.2022.3163682
Booth JBolet G(2023)Neural Acceleration of Graph Based Utility Functions for Sparse MatricesIEEE Access10.1109/ACCESS.2023.326245311(31619-31635)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3262453
Wang YSun LDai L(2019)Convolutional Neural Network Single-Point Control ModelProceedings of the International Conference on Industrial Control Network and System Engineering Research10.1145/3333581.3333587(18-21)Online publication date: 15-Mar-2019
https://dl.acm.org/doi/10.1145/3333581.3333587

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