<![CDATA[Social media has become an inseparable part of daily life in today's digital era. Many parents frequently share photos of their children online, exposing them to risks related to privacy and security. This research addresses such issues by developing an Android-based facial de-identification application that utilises the YOLOv8 algorithm to protect minors' privacy. The methodology involves several stages: data collection, pre-processing, model training, and application development. The dataset includes over 2,889 images of children, which were augmented to enhance its size and diversity. YOLOv8, a state-of-the-art object detection algorithm, was trained with these images to achieve high precision and recall in identifying children's faces. The developed application integrates the YOLOv8 model within a user-friendly interface built with Flutter. Results indicate that YOLOv8 effectively detects children's faces with a high precision of 95%, accuracy of 88%, recall of 92%, and mAP50 of 0.977. While the model demonstrates strong performance on training data, there is room for improvement on unseen data. By leveraging YOLOv8 and providing an accessible mobile application, the work allows parents to protect their children's identities online. The application mitigates risks of unauthorised use and exploitation of children's images by enabling facial de-identification, thus promoting safer online practices for families.]]>
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