Implementation of an Intellectual Property Theft Detection System for Safeguarding Digital Content

Abstract

Intellectual Property (IP) protection is a critical concern, driving the development of Intellectual Property Theft Detection Systems (IPTDS). However, the vast volume of digital content and the evolving tactics of offenders expose the limitations of conventional detection systems. These challenges necessitate advanced solutions capable of adapting to the complexities of the digital landscape. This study aims to design and develop an advanced IPTDS framework for detecting digital IP theft. The framework is trained on data up to October 2023, with key objectives including enhanced detection reliability, reduced false positive rates, and optimized computational efficiency to accommodate modern data scales. The IPTDS framework is implemented in Python, leveraging modern algorithms and web technologies. Its development is guided by existing literature, established practices, and real-world scenarios. Performance evaluation is conducted using key metrics such as detection rate, false positive rate, and computational efficiency. Results indicate that the proposed IPTDS outperforms state-of-the-art solutions in detection accuracy while significantly reducing false positives. Additionally, it meets computational efficiency benchmarks, confirming its practical applicability. This paper presents a highly efficient IPTDS, offering a novel, preventive, and secure approach to IP protection in the digital era. By equipping content creators and innovators with effective detection tools, the system strengthens IP security and fosters greater respect for intellectual property rights across various domains.