Published Paper Details:

The digital era has seen an explosion of online video platforms, which has, in turn, amplified the risk of content piracy, posing significant challenges to content creators and distributors. This research focuses on mitigating unauthorized distribution by developing a system to match video frames across various online platforms. With Computer Vision, we introduce a robust and scalable framework that employs advanced image processing and machine learning techniques to accurately identify and match frames from moving images, despite differences in format, resolution, and compression. The proposed framework incorporates feature extraction, frame hashing, and deep learningbased similarity assessment to ensure 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 and insights into developing crossplatform content identification systems, contributing to more secure and reliable digital media distribution.