TY - GEN
T1 - A Novel Facial Manipulation Detection Method Based on Contrastive Learning
AU - Ma, Zhiyuan
AU - Xu, Pengxiang
AU - Mei, Xue
AU - Shen, Jie
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Nowadays, numerous synthesized face-swapping videos generated by face forgery algorithms have become an emerging problem, which promotes facial manipulation detection to be a significant topic. With the development of face forgery algorithms, some fake face images or videos generated by those strong forgery algorithms are very realistic, which have brought much difficulty to facial manipulation detection. In this paper, we present a novel facial manipulation detection method based on contrastive learning. We analyze theure features of manipulated facial images and propose to compare and learn the features of the whole face and the center face in order to get more general features. We calculate the similarity and distribution distance between the whole face and the center face. The experiments implemented on FaceForensics++ dataset demonstrate that the proposed method achieves outstanding results and can learn the general features.
AB - Nowadays, numerous synthesized face-swapping videos generated by face forgery algorithms have become an emerging problem, which promotes facial manipulation detection to be a significant topic. With the development of face forgery algorithms, some fake face images or videos generated by those strong forgery algorithms are very realistic, which have brought much difficulty to facial manipulation detection. In this paper, we present a novel facial manipulation detection method based on contrastive learning. We analyze theure features of manipulated facial images and propose to compare and learn the features of the whole face and the center face in order to get more general features. We calculate the similarity and distribution distance between the whole face and the center face. The experiments implemented on FaceForensics++ dataset demonstrate that the proposed method achieves outstanding results and can learn the general features.
KW - Contrastive Learning
KW - Deep learning
KW - Face Forgery Detection
KW - Facial Manipulation Detection
KW - Siamese Network
UR - http://www.scopus.com/inward/record.url?scp=85136335205&partnerID=8YFLogxK
U2 - 10.1109/ICET55676.2022.9825156
DO - 10.1109/ICET55676.2022.9825156
M3 - 会议稿件
AN - SCOPUS:85136335205
T3 - 2022 IEEE 5th International Conference on Electronics Technology, ICET 2022
SP - 1163
EP - 1167
BT - 2022 IEEE 5th International Conference on Electronics Technology, ICET 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Electronics Technology, ICET 2022
Y2 - 13 May 2022 through 16 May 2022
ER -