A Robust Algorithm for Detecting Web Content Changes Using Keypoint Matching

Monitoring websites and social media profiles for tracking changes is a critical task in various domains. Traditional methods often rely on textual data (HTML), which may be inaccessible due to data privacy issues. In this paper, we introduce a novel algorithm for detecting changes and localizing them on websites by leveraging the latest and previous images of the web content. This approach circumvents the limitations of textual data extraction and avoids the need to annotate data for deep learning models. The proposed algorithm centers on employing computer vision techniques, specifically keypoints detection and matching, to identify changes in website content. This includes identifying textual and non-textual modifications on websites and providing a comprehensive solution for monitoring online content. The presented approach offers a practical and privacy-conscious method for tracking changes on websites and social media profiles, enhancing the ability to monitor dynamic online content efficiently.