최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Journal of Korean Society of Industrial and Systems Engineering = 한국산업경영시스템학회지, v.44 no.4, 2021년, pp.193 - 207
김량형 (한밭대학교 산업경영공학과) , 김태구 (한밭대학교 산업경영공학과)
As Deepfakes phenomenon is spreading worldwide mainly through videos in web platforms and it is urgent to address the issue on time. More recently, researchers have extensively discussed deepfake video datasets. However, it has been pointed out that the existing Deepfake datasets do not properly ref...
Dolhansky, B., Howes, R., Pflaum, B., Baram, N., and Ferrer, C. C., The Deepfake detection challenge (dfdc) preview dataset, arXiv preprint arXiv:1910.08854., 2019.
Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., and Ferrer, C.C., The Deepfake detection challenge (dfdc) dataset, arXiv preprint arXiv:2006.07397, 2020.
Dordevic, M., Milivojevic, M., and Gavrovska, A., Deepfake video analysis using SIFT features, In 2019 27th Telecommunications Forum (TELFOR), IEEE, 2019, November, pp. 1-4.
Dufour, N. and Gully, A., Contributing data to deep-fake detection research, 2019. URL https://ai.googleblog.com/2019/09/contributing-data-to-Deepfake-detection.html.
Feng, K., Wu, J., and Tian, M., A Detect method for Deepfake video based on full face recognition, In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), IEEE, 2020, November, Vol. 1, pp. 1121-1125.
Guarnera, L., Giudice, O., and Battiato, S., Fighting Deepfake by exposing the convolutional traces on images, IEEE Access, 2020, Vol. 8, pp. 165085-165098.
Jiang, L., Li, R., Wu, W., Qian, C., and Loy, C. C., Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 2889-2898.
Karras, T., Aila, T., Laine, S., and Lehtinen, J., Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017.
Kim, T.G., Kim, D.H., Lee, D.J., Kim, K.T., and Moon, S.D.S., Development of Checklist to Promote Commercialization of R&D for Railway Transportation using AHP method, Journal of the Korea Management Engineers Society, 2017, Vol. 22, No. 1, pp. 77-87.
KOREA Data Agency, Guideline for data quality management(Ver 2.1), 2006.
Korshunov, P., and Marcel, S., Vulnerability assessment and detection of Deepfake videos, In 2019 International Conference on Biometrics (ICB), IEEE, 2019, June, pp. 1-6.
Kwon, P., You, J., Nam, G., Park, S., and Chae, G., KoDF: A Large-scale Korean Deepfake Detection Dataset, arXiv preprint arXiv:2103.10094, 2021.
Le, T. N., Nguyen, H. H., Yamagishi, J., and Echizen, I. OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10117-10127.
Li, Y., and Lyu, S., Exposing Deepfake videos by detecting face warping artifacts, ArXiv Preprint ArXiv:1811.00656, 2018.
Li, Y., Yang, X., Sun, P., Qi, H., and Lyu, S., Celeb-df: A large-scale challenging dataset for Deepfake forensics, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3207-3216.
Mahfoudi, G., Tajini, B., Retraint, F., Morain-Nicolier, F., Dugelay, J.L., and Marc, P.I.C., DEFACTO: Image and face manipulation dataset, In 2019 27th European Signal Processing Conference (EUSIPCO), IEEE, 2019, September, pp. 1-5.
Matern, F., Riess, C., and Stamminger, M., Exploiting visual artifacts to expose Deepfakes and face manipulations, In 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), IEEE, 2019, January, pp. 83-92.
National Information Society Agency, Guideline for artificial intelligence learning data quality management, 2020.
Ramadhani, K.N. and Munir, R., A Comparative Study of Deepfake Video Detection Method, In 2020 3rd International Conference on Information and Communications Technology (ICOIACT), IEEE, 2020, November, pp. 394-399.
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Niessner, M., Faceforensics++: Learning to detect manipulated facial images, In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 1-11.
Saaty, T.L., Axiomatic foundation of the analytic hierarchy process, Management Science, 1986, Vol. 32, No. 7, pp. 841- 855.
Tayseer, M., Mohammad, J., Ababneh, M., Al-Zoube, A., and Elhassan, A., Digital Forensics and Analysis of Deepfake Videos, In 11th International Conference on Information and Communication Systems (ICICS), 2020.
Thies, J., Zollhofer, M., and Niessner, M., Deferred neural rendering, Image synthesis using neural textures. ACM Transactions on Graphics (TOG), 2019, Vol. 38, No. 4, pp. 1-12.
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., and Niessner, M., Face2face: Real-time face capture and reenactment of rgb videos, In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2387-2395.
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., and Ortega-Garcia, J., Deepfakes and beyond: A survey of face manipulation and fake detection, Information Fusion, 2020, Vol. 64, pp. 131-148.
Verdoliva, L., Media Forensics and Deepfakes: An Overview, IEEE Journal of Selected Topics in Signal Processing, 2020, Vol. 14, No. 5, pp. 910-932.
Wang, R., Juefei-Xu, F., Ma, L., Xie, X., Huang, Y., Wang, J., and Liu, Y., Fakespotter: A simple yet robust baseline for spotting ai-synthesized fake faces, ArXiv Preprint ArXiv:1909.06122, 2019.
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., and Catanzaro, B., High-resolution image synthesis and semantic manipulation with conditional gans, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8798-8807.
Youm, G.Y. and Kim, M.C., SAR image target recognition research trend using deep learning, The Journal of The Korean Institute of Communication Sciences, 2017, Vol. 34, No. 7, pp. 31-39.
Yu, P., Xia, Z., Fei, J., and Lu, Y., A Survey on Deepfake Video Detection, IET Biometrics, 2016, October.
Zi, B., Chang, M., Chen, J., Ma, X., and Jiang, Y.G., WildDeepfake: A challenging real-world dataset for Deepfake detection, In Proceedings of the 28th ACM International Conference on Multimedia, 2020, October, pp. 2382-2390.
UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/datasets.php.
Kaggle, https://www.kaggle.com/.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.