Optimized Digital Watermarking for Robust Information Security in Embedded Systems

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Publicat a:Information vol. 16, no. 4 (2025), p. 322
Autor principal: Mohcin, Mekhfioui
Altres autors: El Bazi Nabil, Laayati Oussama, Satif Amal, Bouchouirbat Marouan, Kissi Chaïmaâ, Boujiha Tarik, Chebak Ahmed
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MDPI AG
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035 |a 3194615295 
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100 1 |a Mohcin, Mekhfioui  |u Green Tech Institute (GTI), Mohammed VI Polytechnic University, Benguerir 43150, Morocco 
245 1 |a Optimized Digital Watermarking for Robust Information Security in Embedded Systems 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a With the exponential growth in transactions and exchanges carried out via the Internet, the risks of the falsification and distortion of information are multiplying, encouraged by widespread access to the virtual world. In this context, digital image watermarking has emerged as an essential solution for protecting digital content by enhancing its durability and resistance to manipulation. However, no current digital watermarking technology offers complete protection against all forms of attack, with each method often limited to specific applications. This field has recently benefited from the integration of deep learning techniques, which have brought significant advances in information security. This article explores the implementation of digital watermarking in embedded systems, addressing the challenges posed by resource constraints such as memory, computing power, and energy consumption. We propose optimization techniques, including frequency domain methods and the use of lightweight deep learning models, to enhance the robustness and resilience of embedded systems. The experimental results validate the effectiveness of these approaches for enhanced image protection, opening new prospects for the development of information security technologies adapted to embedded environments. 
610 4 |a Raspberry Pi Ltd 
653 |a Digital imaging 
653 |a Data integrity 
653 |a Embedded systems 
653 |a Digital watermarking 
653 |a Smartphones 
653 |a Tattoos 
653 |a Wavelet transforms 
653 |a Security 
653 |a Artificial intelligence 
653 |a Multimedia 
653 |a Communication 
653 |a Intellectual property 
653 |a Optimization techniques 
653 |a Signal processing 
653 |a Neural networks 
653 |a Digital watermarks 
653 |a Data encryption 
653 |a Deep learning 
653 |a Energy consumption 
653 |a Optimization algorithms 
653 |a Efficiency 
700 1 |a El Bazi Nabil  |u Green Tech Institute (GTI), Mohammed VI Polytechnic University, Benguerir 43150, Morocco 
700 1 |a Laayati Oussama  |u Green Tech Institute (GTI), Mohammed VI Polytechnic University, Benguerir 43150, Morocco 
700 1 |a Satif Amal  |u National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco 
700 1 |a Bouchouirbat Marouan  |u Green Tech Institute (GTI), Mohammed VI Polytechnic University, Benguerir 43150, Morocco 
700 1 |a Kissi Chaïmaâ  |u National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco 
700 1 |a Boujiha Tarik  |u National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco 
700 1 |a Chebak Ahmed  |u Green Tech Institute (GTI), Mohammed VI Polytechnic University, Benguerir 43150, Morocco 
773 0 |t Information  |g vol. 16, no. 4 (2025), p. 322 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3194615295/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3194615295/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3194615295/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch