Preserving Copyright Integrity: A Comprehensive Approach to Combat YouTube Video Infringement and Privacy-Sensitive Video Analysis

Abstract

Addressing the pressing issue of YouTube video infringement, which has become increasingly prevalent in the digital age, this study aims to uncover its underlying causes and explore potential solutions. Through a comprehensive analysis, explore the challenges posed by copyright violations on YouTube and propose strategies to detect and combat infringement effectively. By leveraging advanced techniques in multimedia forensics and legal frameworks, our approach aims to protect the rights of content creators and uphold copyright integrity in online video content. Propose a novel approach for video representation and classification, focusing on privacy-preserving techniques and robust performance. Our methodology begins with pre-processing using Gabor filters and key frame selection to enhance feature extraction. Feature extraction is performed using Petri Net and Horse Herd optimization (HHO) algorithms to capture intricate patterns in the data. For video representation and classification, employ a BiLSTM network with an attention mechanism, followed by a softmax classifier for accurate classification. This comprehensive framework offers a robust solution for privacy-sensitive video analysis across various domains. The proposed method leveraging Python tools achieves a remarkable accuracy of 99% when applied to a dataset extracted from YouTube videos. This study underscores the efficacy of the approach in effectively analyzing and classifying video content, offering promising prospects for various applications in video analysis and understanding.