Published Paper Details:

With the surge of online video platforms in the digital age, the risk of content piracy has significantly increased, posing serious challenges for content creators and distributors. This research aims to counteract unauthorized distribution by developing a system that matches video frames across various online platforms. Using advanced computer vision techniques, we propose a robust and scalable framework that combines image processing and machine learning to accurately identify and match frames from moving images, regardless of differences in format, resolution, and compression. Our framework includes essential components such as feature extraction, frame hashing, and deep learning-based similarity assessment to achieve high precision and recall in detecting pirated content. Extensive experiments on diverse datasets demonstrate our approach's superior accuracy and computational efficiency compared to existing methods. This study offers a comprehensive solution to video piracy, providing valuable insights into the development of cross-platform content identification systems. By enhancing the security and reliability of digital media distribution, our work contributes to protecting the rights of content creators and ensuring the integrity of online video content.