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Visually meaningful image encryption using convolution neural networks [PDF]
Scientific ReportsImage encryption techniques are broadly used to protect confidential images during transmission and storage. However, conventional image encryption techniques typically produce noisy ciphertext images that can easily reveal the presence of encrypted ...
Varsha Himthani +2 more
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HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks [PDF]
Sensors, 2021 Kyung Soo Kim +2 more
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Research Progress of Lightweight Neural Network Convolution Design [PDF]
Jisuanji kexue yu tansuo, 2022 Traditional neural networks have the disadvantages of over-reliance on hardware resources and high requirements for application equipment performance. Therefore, they cannot be deployed on edge devices and mobile terminals with limited computing power ...
MA Jinlin, ZHANG Yu, MA Ziping, MAO Kaiji
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Contextual Convolutional Neural Networks [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the standard convolution, which is the core component of convolutional neural networks. CoConv is implicitly equipped with the capability of incorporating contextual information while maintaining a similar number of parameters and computational cost ...
Ionut Cosmin Duta +2 more
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A Study on Defect Detection Model of Bone Plates Using Multiple Filter CNN of Parallel Structure [PDF]
한국정밀공학회지, 2023 Bone plates are a medical device used for fixing broken bones, which should not have a crack and hole defect. Defect detection is very important because bone plate defect is very dangerous.
Song Yeon Lee, Yong Jeong Huh
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A Remote Sensing Image Semantic Segmentation Method by Combining Deformable Convolution with Conditional Random Fields [PDF]
Journal of Geodesy and Geoinformation Science, 2020 Currently, deep convolutional neural networks have made great progress in the field of semantic segmentation. Because of the fixed convolution kernel geometry, standard convolution neural networks have been limited the ability to simulate geometric ...
ZUO Zongcheng,ZHANG Wen,ZHANG Dongying
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Frontiers in Materials, 2021 This paper proposes a novel neural network architecture and its ensembles to predict the critical superconductivity temperature of materials based on their chemical formula.
Dmitry Viatkin +4 more
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Novel Preprocessors for Convolution Neural Networks
IEEE Access, 2022 Fooling neural networks is a main concern in the process of Artificial Intelligence optimization. Character perturbation make part of a text unnoticeable for some systems, even for human observers.
Ziad Doughan +3 more
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Frontiers in Public Health, 2022 Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face ...
Muhammad Zubair Asghar +12 more
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A Stacking Algorithm for Convolution Neural Network [PDF]
Jisuanji gongcheng, 2018 In order to improve the classification accuracy of convolution neural network,an improved Stacking algorithm combining multiple convolution neural networks is proposed.The convolution neural network is used as the base classifier to classify the data,and
ZHANG Xiaoming,WANG Zhijun,LIANG Liping
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