• The Journal of Information Systems
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• v.16 no.1
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• pp.135-158
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• 2007

The purpose of this research is to identify multidimensional motivation factors that determine the piracy of copyrighted digital music. The model is based on TPB(theory of planned behavior) as well as other models in consumer behavior. An empirical study resulted in the following findings. first Both individual's attitude toward music piracy and individual's perceived behavior control have positive impacts on the individual's behavioral intention of piracy. It turned out that perceived behavior control has a stronger impact on behavioral intention than attitude does. Second, the level of individual's moral judgment has negative impacts on both the attitude and behavioral intention toward music piracy. Third, individual's experience in music piracy positively affects the attitude, but does not directly or indirectly affect the behavior intention. Fourth, an economic gain from music piracy is not a significant factor in determining both attitude and behavioral intention. Fifth, the risk of being prosecuted for music piracy is a major factor in determining one's attitude, although the risk is not significant enough to change one's behavioral intention. This research found that individuals' intention to pirate digital music is mainly affected by the moral and ethical standards of the individuals and by the extra resources and abilities they possess. Such factors as economic gain and law enforcement were not significant enough to alter one's behavioral intention. This research is significant in that it established a behavioral model to understand the piracy of copyrighted digital music and that it empirically tested the model with Internet users in Korea. This is one of the first empirical studies in Korea to touch such ethically and perhaps politically sensitive issues as online music piracy.

• Journal of IKEEE
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• v.24 no.2
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• pp.419-425
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• 2020

The market size of immersive 360-degree video contents, which are noted as one of the main technology of the fourth industry, increases every year. However, since most of the images are distributed through illegal distribution networks such as Torrent after the DRM gets lifted, the damage caused by illegal copying is also increasing. Although filtering technology is used as a technology to respond to these issues in 2D videos, most of those filtering technology has issues in that it has to overcome the technical limitation such as huge feature-point data volume and the related processing capacity due to ultra high resolution such as 4K UHD or higher in order to apply the existing technology to immersive 360° videos. To solve these problems, this paper proposes a feature-point recognition ratio improvement method for immersive 360-degree videos using deep learning technology.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.55-62
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• 2019

The needs for small size and low power consumption of information devices is being implemented with SOC technology that implements the program on a single chip in Internet of Thing. Copyright disputes due to piracy are increasing in semiconductor chips as well, arising from disputes in the chip implementation of the design house and chip implementation by the illegal use of the source code. However, since the final chip implementation is made in the design house, it is difficult to protect the copyright. In this paper, we deal with the analysis method for extracting similarity and the criteria for setting similarity judgment in the dispute of source code written in HDL language. Especially, the chip which is manufactured based on the same specification will be divided into the same configuration and the code type.

• Journal of IKEEE
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• v.24 no.2
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• pp.529-535
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• 2020

As the main technology of the 4th industrial revolution, immersive 360-degree video contents are drawing attention. The market size of immersive 360-degree video contents worldwide is projected to increase from $6.7 billion in 2018 to approximately $70 billion in 2020. However, most of the immersive 360-degree video contents are distributed through illegal distribution networks such as Webhard and Torrent, and the damage caused by illegal reproduction is increasing. Existing 2D video industry uses copyright filtering technology to prevent such illegal distribution. The technical difficulties dealing with immersive 360-degree videos arise in that they require ultra-high quality pictures and have the characteristics containing images captured by two or more cameras merged in one image, which results in the creation of distortion regions. There are also technical limitations such as an increase in the amount of feature point data due to the ultra-high definition and the processing speed requirement. These consideration makes it difficult to use the same 2D filtering technology for 360-degree videos. To solve this problem, this paper suggests a feature point extraction and identification technique that select object identification areas excluding regions with severe distortion, recognize objects using deep learning technology in the identification areas, extract feature points using the identified object information. Compared with the previously proposed method of extracting feature points using stitching area for immersive contents, the proposed technique shows excellent performance gain.