Abstract
In the age of digital media, securing personal identities in shared material, especially on social media, has become a significant challenge. This research leverages software engineering to automate face blurring in photographs of Arab social media personalities. It proposes a system that integrates sophisticated deep-learning algorithms with standard image processing within a robust software architecture. This modular system is scalable, maintainable, and compatible with digital media platforms. Gaussian blur is applied to protect privacy once convolutional neural networks (CNNs) identify faces. The system’s efficiency and accuracy are enhanced by OpenCV and NumPy. In experiments, this system consistently identifies and blurs faces with over 90% accuracy, making it a reliable tool for digital media privacy. The system’s modularity and flexibility enable its easy integration and modification across applications, in adherence to software engineering principles such as reusability, scalability, and maintainability.
Recommended Citation
Mohialden, Yasmin Makki; Hussien, Nadia Mahmood; and Mohammed, Mostafa Abdulghafoor
(2025)
"Software Engineering Approach to Enhancing Privacy Protection: Automated Face Blurring Using Deep Learning in Arab Social Media,"
Iraqi Journal for Computer Science and Mathematics: Vol. 6:
Iss.
3, Article 43.
DOI: https://doi.org/10.52866/2788-7421.1317
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/43
