Semi-U-Net: A Lightweight Deep Neural Network for Subject-Sensitive Hashing of HRRS Images

As a special case of perceptual hashing algorithm, subject-sensitive hashing can realize "subject-biased" integrity authentication of high resolution remote sensing (HRRS) images, which overcomes the deficiencies of existing integrity authentication technologies. However, the existing deep...

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Veröffentlicht in:IEEE access Jg. 9; S. 60280 - 60295
Hauptverfasser: Ding, Kaimeng, Su, Shoubao, Xu, Nan, Jiang, Tingting
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial neural networks
Authentication
Computational efficiency
Computational modeling
Computing costs
Convolution
Cryptography
Data mining
Feature extraction
Hash based algorithms
HRRS image
Image resolution
Integrity
integrity authentication
lightweight deep neural network
Mathematical models
Neural networks
Parameters
Remote sensing
Subject-sensitive hashing
U-net
ISSN:2169-3536, 2169-3536
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Zusammenfassung:As a special case of perceptual hashing algorithm, subject-sensitive hashing can realize "subject-biased" integrity authentication of high resolution remote sensing (HRRS) images, which overcomes the deficiencies of existing integrity authentication technologies. However, the existing deep neural network for subject-sensitive hashing have disadvantages such as high model complexity and low computational efficiency. In this paper, we propose an efficient and lightweight deep neural network named Semi-U-net to achieve efficient subject-sensitive hashing. The proposed Semi-U-net realizes the lightweight of the network from three aspects: First, considering the general process of perceptual hashing, it adopts a semi-u-shaped structure, which simplify the model structure and prevent the model from extracting too much redundant information to enhance the robustness of the algorithm; Second, the number of model parameters and the computational cost are significantly reduced by using deep separable convolution in the entire asymmetric network; Third, the number of model parameters is further compressed by using the dropout layer several times. The experimental results show that the size of our Semi-U-Net model is only 5.38M, which is only 1/27 of MUM-net and 1/15 of MultiResUnet. The speed of the Semi-U-Net based subject-sensitive hashing algorithm is 88.6 FPS, which is 2.89 times faster than MultiResUnet based algorithm and 2.1 times faster than MUM-net Based Algorithm. FLOPs of Semi-U-net is only 1/28 of MUM-net and 1/16 of MultiResUnet.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3074055