발간년도 : [2022]
| 논문정보 |
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| 논문명(한글) |
[Vol.17, No.6] A Study on the Forgery and Detection of CT Data Using Deep Learning Model |
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| 논문투고자 |
Hyun-Seong Lee, Jin-A Lee, Jeong-A Han, Min-Ji Choi, Ho-Jung Song, Yong-Do Her |
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| 논문내용 |
Currently, research on deep learning models is being actively conducted, and many studies are being conducted in various fields. In the medical field, research is also being conducted using various medical data such as medical images and electronic medical records. In particular, research is underway to solve research problems in the medical field due to lack of data by using image conversion technology. However, through highly developed AI models, forged images can cause many problems. If forged images are abused in the medical field, it is a big problem that can endanger patients' lives. In this paper, the Cycle-GAN forgery model is constructed using CT images to solve and prevent problems that may arise through forgery images generated through artificial intelligence models. After that, the quality evaluation of the forged image was conducted. In addition, forgery image detection was performed through a forgery detection model composed of a Concat block, and detection performance was quantified with a classification performance evaluation index. As a result of classification performance indicators, each indicator showed a high value of more than 99%. Through this, it can be seen that the CT image forgery detection model of this paper has excellent performance. In the future, research will be conducted using various medical data as well as CT images, and the detection of forged data will be conducted in various ways. |
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