Convolution Neural Network with Dual Tree Complex Wavelet Transform Preprocessor for Universal Image Steganalysis

This paper presents a new validation method using a convolutional neural network for palm vein recognition. Unlike fingerprint and face. Vein patterns are endogenous biometric features that do not change over time and that make them difficult to identify and replicate in people. The proposed paper aims to provide a new way to identify people through their veins. This paper used the CASIA dataset, which consists of several wavelengths, in this research used the 850nm wavelength, which is clear in the veins, In addition, we divided the data into 3 cases. The first case is when the training and testing ratio is 50/50, the second case when it is 70/30, and the last case when it is 90/10. Obtained an accuracy of 98% in the case 90/10. In addition, to the proposed network, and used a well-known global network, the AlexNet network, where did the same work on it to compare the results of our proposed network with it. As proposed network outperformed it in terms of accuracy and speed, where the accuracy was 96% in the case 90/10.

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“…Where 𝑥𝑘 the corresponding production of category k and j is the total number of classes [10] [15] [13].…”

Section: Activation Functionsmentioning

confidence: 99%

Palm vein recognition based on convolution neural network

Al-jaberi

Al-juboori

2021

J. Al-Qadisiyah Comp. Sci. Math.

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Effect of levels in Dual Tree Complex Wavelet Transform when design Universal image stego-analytic

Mohsen

Hasan

2020

eijs

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Universal image stego-analytic has become an important issue due to the natural images features curse of dimensionality. Deep neural networks, especially deep convolution networks, have been widely used for the problem of universal image stegoanalytic design. This paper describes the effect of selecting suitable value for number of levels during image pre-processing with Dual Tree Complex Wavelet Transform. This value may significantly affect the detection accuracy which is obtained to evaluate the performance of the proposed system. The proposed system is evaluated using three content-adaptive methods, named Highly Undetetable steGO (HUGO), Wavelet Obtained Weights (WOW) and UNIversal WAvelet Relative Distortion (UNIWARD). The obtained precision 0.98387, 0.96659 and 0.98387 for the three content-adaptive methods, applied on BOSS image dataset, respectively. The obtained results show that number of level equals to 5 outperforms other numbers in terms of detection accuracy. Also it minimizes the ime required for both training and testing phases.

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Convolution Neural Network with Dual Tree Complex Wavelet Transform Preprocessor for Universal Image Steganalysis

Cited by 2 publications

References 23 publications

Palm vein recognition based on convolution neural network

Palm vein recognition based on convolution neural network

Effect of levels in Dual Tree Complex Wavelet Transform when design Universal image stego-analytic

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