• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1067-1078
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• 2020

With the development of internet technology, a lot of content is produced, and the demand for it is increasing. Accordingly, the number of contents in circulation is increasing, while the number of distributing illegal copies that infringe on copyright is also increasing. The Korea Copyright Protection Agency operates a illegal content obstruction program based on substring matching, and it is difficult to accurately search because a large number of noises are inserted to bypass this. Recently, researches using natural language processing and AI deep learning technologies to remove noise and various blockchain technologies for copyright protection are being studied, but there are limitations. In this paper, noise is removed from data collected online, and keyword-based illegal copies are searched. In addition, the same heavy uploader is estimated through profiling analysis for heavy uploaders. In the future, it is expected that copyright damage will be minimized if the illegal copy search technology and blocking and response technology are combined based on the results of profiling analysis for heavy uploaders.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2115-2130
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• 2022

With the development of over-the-top (OTT) services, the demand for content is increasing, and you can easily and conveniently acquire various content in the online environment. As a result, copyrighted content can be easily copied and distributed, resulting in serious copyright infringement. Some special forms of online service providers (OSP) use filtering-based technologies to protect copyrights, but illegal uploaders use methods that bypass traditional filters. Uploading with a title that bypasses the filter cannot use a similar search method to detect illegal content. In this paper, we propose a technique for profiling the Heavy Uploader by normalizing the bypassed content title and efficiently detecting illegal content. First, the word is extracted from the normalized title and converted into a bit-array to detect illegal works. This Bloom Filter method has a characteristic that there are false positives but no false negatives. The false positive rate has a trade-off relationship with processing performance. As the false positive rate increases, the processing performance increases, and when the false positive rate decreases, the processing performance increases. We increased the detection rate by directly comparing the word to the result of increasing the false positive rate of the Bloom Filter. The processing time was also as fast as when the false positive rate was increased. Afterwards, we create a function that includes information about overall piracy and identify clustering-based heavy uploaders. Analyze the behavior of heavy uploaders to find the first uploader and detect the source site.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.75-83
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• 2021

With the development of the IT industry and the increase of cultural activities, the demand for works increases, and they can be used easily and conveniently in an online environment. Accordingly, copyright infringement is seriously occurring due to the ease of copying and distribution of works. Some special types of Online Service Providers (OSP) use filtering-based technology to protect copyrights, but they can easily bypass them, and there are limits to blocking all illegal works, making it increasingly difficult to protect copyrights. Recently, most of the distributors of illegal works are a certain minority, and profits are obtained by distributing illegal works through many OSP and majority ID. In this paper, we propose a profiling technique for heavy uploader, which is a major analysis target based on illegal works. Creates a feature containing information on overall illegal works and identifies major heavy uploader. Among these, clustering technology is used to identify heavy uploader that are presumed to be the same person. In addition, heavy uploaders with high priority can be analyzed through illegal work Distributor tracking and behavior analysis. In the future, it is expected that copyright damage will be minimized by identifying and blocking heavy uploader that distribute a large amount of illegal works.