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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.12
- 수록면
- 1,624 - 1,631 (8page)
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초록· 키워드
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With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.
목차
ABSTRACT
1. 서론
2. 관련 연구
3. 일반화 능력 향상
4. 실험 및 분석
5. 결론
REFERENCE
참고문헌 (17)
참고문헌 신청
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and Improving the Image Quality of StyleGAN,” Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110-8119, 2020.
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, and W. Shi, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681-4690, 2017.
J.Y. Zhu, T. Park, P. Isola, and A.A. Efros, “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks,” Proceeding of the IEEE International Conference on Computer Vision, pp. 2223-2232, 2017.
H. Mo, B. Chen, and W. Luo, “Fake Faces Identification via Convolutional Neural Network,” Proceeding of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp. 43-47, 2018.
N.T. Do, I.S. Na, and S.H. Kim, “Forensics Face Detection from GANs Using Convolutional Neural Network,” Proceeding of International Symposium on Information Technology Convergence, 2018.
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, and W. Shi, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681-4690, 2017.
J.Y. Zhu, T. Park, P. Isola, and A.A. Efros, “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks,” Proceeding of the IEEE International Conference on Computer Vision, pp. 2223-2232, 2017.
H. Mo, B. Chen, and W. Luo, “Fake Faces Identification via Convolutional Neural Network,” Proceeding of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp. 43-47, 2018.
N.T. Do, I.S. Na, and S.H. Kim, “Forensics Face Detection from GANs Using Convolutional Neural Network,” Proceeding of International Symposium on Information Technology Convergence, 2018.
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