Relating brain structure images to personality characteristics using 3D convolution neural network

Cao, Lixian; Lv, Wei; Park, Kaechang; Miura, Yasühiro; Shinomiya, Yuki; Yoshida, Shigeaki

doi:10.1049/cit2.12021

Cited by 3 publications

(2 citation statements)

References 26 publications

(28 reference statements)

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“…With the development of deep learning, it has become a research hotspot for scholars in computer vision, natural language processing, speech recognition, and other fields, especially Convolutional Neural Networks (CNN) is currently very widely used, and more stable feature information can be extracted by convolutional neural networks [22,23]. Therefore, in response to the weakness of traditional algorithms in resisting geometric attacks, some research scholars have combined deep learning techniques with watermarking technology in recent years to improve the robustness of watermarking algorithms, which has essential research significance for medical privacy protection.…”

Section: Weak Robustness Against Geometric Attacksmentioning

confidence: 99%

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

Zhang¹,

Li²,

Bhatti³

et al. 2023

Computers, Materials &Amp; Continua

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The field of medical images has been rapidly evolving since the advent of the digital medical information era. However, medical data is susceptible to leaks and hacks during transmission. This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage, as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms. First, a pre-trained GoogLeNet network is used in this paper, based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset, so that the pre-trained network can further learn the deep convolutional features in the medical dataset, and then the trained network is used to extract the stable feature vectors of medical images. Then, a two-dimensional Henon chaos encryption technique, which is more sensitive to initial values, is used to encrypt multiple different types of watermarked private information. Finally, the feature vector of the image is logically operated with the encrypted multiple watermark information, and the obtained key is stored in a third party, thus achieving zero watermark embedding and blind extraction. The experimental results confirm the robustness of the algorithm from the perspective of multiple types of watermarks, while also demonstrating the successful embedding of multiple watermarks for medical images, and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

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Section: Weak Robustness Against Geometric Attacksmentioning

confidence: 99%

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

Zhang¹,

Li²,

Bhatti³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Due to their strong learning ability, CNNs are often exploited for image applications [18,19]. For example, CNNs are used to extract robust features for better representing watermarks for watermark removal [20].…”

Section: Deep Cnns For Watermark Removalmentioning

confidence: 99%

An Improved U-Net for Watermark Removal

Shi

Sun

et al. 2022

Electronics

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Convolutional neural networks (CNNs) with different layers have performed with excellent results in watermark removal. However, how to extract robust and effective features via CNNs of black box in watermark removal is very important. In this paper, we propose an improved watermark removal U-net (IWRU-net). Taking the robustness of obtained information into account, a serial architecture is designed to facilitate useful information for guaranteeing performance in watermark removal. Taking the problem of long-term dependency into account, U-nets based simple components are integrated into the serial architecture to extract more salient hierarchical information for addressing watermark removal problems. To increase the adaptability of IWRU-net to the real world, we use randomly distributed blind watermarks to implement a blind watermark removal model. The experiment results illustrate that the proposed method is superior to other popular watermark removal methods in terms of quantitative and qualitative evaluations.

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DS-UNeXt: depthwise separable convolution network with large convolutional kernel for medical image segmentation

Huang

Chen

Jiang

2022

SIViP

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Relating brain structure images to personality characteristics using 3D convolution neural network

Cited by 3 publications

References 26 publications

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

An Improved U-Net for Watermark Removal

DS-UNeXt: depthwise separable convolution network with large convolutional kernel for medical image segmentation

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