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Secure Implementation of Image Processing Operations on CPU and GPU Architectures: With Deep Learning Frameworks
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Author(s): C. V. Suresh Babu (Hindustan Institute of Technology and Science, India), J. Vishnupriyan (Hindustan Institute of Technology and Science, India), U. VijaykumarReddy (Hindustan Institute of Technology and Science, India), P. Hariharan (Hindustan Institute of Technology and Science, India)and P. Dheenadhayalan (Hindustan Institute of Technology and Science, India)
Copyright: 2025
Pages: 50
Source title:
Signal and Image Processing Techniques for Defense, Security, and Healthcare
Source Author(s)/Editor(s): B. Omkar Lakshmi Jagan (Vignan's Institute of Information Technology, India), Amrit Mukherjee (University of South Bohemia, Czech Republic), Thayyaba Khatoon Mohammed (Malla Reddy University, India)and Vustikayala Sivakumar Reddy (Malla Reddy University, India)
DOI: 10.4018/979-8-3693-3840-7.ch010
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Abstract
This chapter thoroughly investigates the security and performance impacts of integrating deep learning frameworks with CPU and GPU architectures for image processing. Using a mixed-methods approach, including surveys, trials, and thematic analysis, insights are gathered from experts. Data collection involves performance reviews, surveys, and security evaluations. Statistical and thematic analyses provide insights into performance and security issues. Adherence to ethical standards is emphasized, along with methodological clarity and randomized data collection to ensure research validity. Limitations, such as focusing on specific tasks and hardware configurations, are acknowledged. By integrating quantitative and qualitative data, this study provides a comprehensive view of secure image processing, laying groundwork for future exploration in the field.
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