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Iuon-Chang Lin

As seen in: MDPI
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Articles

A Novel Self-Recovery Fragile Watermarking Scheme Based on Convolutional Autoencoder

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By Chin-Feng Lee, Iuon-Chang Lin, Tong-Ming Li
| preprints.org
This version is not peer-reviewed. Chin-Feng Lee,Tong-Ming Li,Iuon-Chang Lin  *,Anis Ur Rehman  * Chin-Feng Lee,Tong-Ming Li,Iuon-Chang Lin  *,Anis Ur Rehman  * This version is not peer-reviewed. In the digital era where images are easily accessible, concerns about image authenticity and integrity are increasing. To address this, we propose a deep learning-based fragile watermarking method for secure image authentication and content recovery.
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A Self-Sovereign Identity Blockchain Framework for Access Control and Transparency in Financial Institutions

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By Hsia-Hung Ou, Guan-Yu Chen, Iuon-Chang Lin
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Enhancing Data Preservation and Security in Industrial Control Systems through Integrated IOTA Implementation

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By Iuon-Chang Lin, Pai-Ching Tseng, Pin-Hsiang Chen, Shean-Juinn Chiou
| MDPI
1. Introduction With the emergence of Industry 4.0, various innovative industrial technologies, including MES or AI technology, have gained attention. Traditional factories have gradually transformed into intelligent factories, and the IoT also plays an important role in it. As IoT devices generate a large amount of data, security and integrity that satisfy data preservation requirements are increasingly valued. Data preservation technology has become indispensable.
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