• The Journal of the Korea Contents Association
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• v.8 no.9
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• pp.55-65
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• 2008

At these information age when the importance of news is of particular emphasis, the field of image-production for the news are being made rapid progressive by high-tech like multi-media, multi-channel digital system. Even experts who have engaged in the work of broadcasting in th field for a long time are perplexed with rapid development in Broadcasting equipments and expression techniques. The field of TV is characterized by the speed of change and the desire of viewers for new and interesting video images. The image expression system applying image line has ever existed as one of conventional image expression methods. Obsolete and old image expressions are paling into significance for viewers who want to access more information in a short time. but The change of image expression systems due to the progressive stream of time has forced existing imaginary to be changed constantly to accommodate the changing interests and expectations of the viewers. Therefore, in this treatise, we need a broad interpretation about the direction of this imaginary line for TV news image in that existing systems of image producing haven’t also been changed and adapted to the stream of time. In these days, image is defined as not only video, but also audio. also We need to reduce the confusion concerning the imaginary line and contribute to a correct understanding images of TV news for not only customers but also producer by extending and applying the concept of imaginary line to image producing.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.933-943
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• 2022

As the Internet develops and the utilization rate of computers increases, the threats posed by malware keep increasing. This leads to the demand for a system to automatically analyzes a large amount of malware. In this paper, an automatic malware analysis technique using a deep learning algorithm is introduced. Our proposed method uses CNN (Convolutional Neural Network) to analyze the malicious features represented as images. To reflect semantic information of malware for detection, our method uses the opcode frequency data of binary for image generation, rather than using bytes of binary. As a result of the experiments using the datasets consisting of 20,000 samples, it was found that the proposed method can detect malicious codes with 91% accuracy.