TY - GEN
T1 - FACE FORGERY DETECTION BASED ON SEGMENTATION NETWORK
AU - Zhou, Yingbin
AU - Luo, Anwei
AU - Kang, Xiangui
AU - Lyu, Siwei
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Recent progress in facial manipulation technologies have made it hard to distinguish the sophisticated face swapped images/videos. Due to the diversity of generation software and data sources, it is extremely challenging to devise an efficient generality framework. Instead of regarding the detection process as a vanilla binary classification task, we proposed a detection framework based on pixel-level classification. Considering that the acquisition of real pixel-level ground-truth is somehow expensive or even impractical, we proposed a pseudo ground-truth generation pipeline with prior knowledge of facial manipulation. Besides, we added a new module into the neural network to capture frequency clues, while the ablation experiment verified the effectiveness of this module. The experimental results on several public datasets demonstrated that our proposed framework is effective and superior to other existing similar detection networks.
AB - Recent progress in facial manipulation technologies have made it hard to distinguish the sophisticated face swapped images/videos. Due to the diversity of generation software and data sources, it is extremely challenging to devise an efficient generality framework. Instead of regarding the detection process as a vanilla binary classification task, we proposed a detection framework based on pixel-level classification. Considering that the acquisition of real pixel-level ground-truth is somehow expensive or even impractical, we proposed a pseudo ground-truth generation pipeline with prior knowledge of facial manipulation. Besides, we added a new module into the neural network to capture frequency clues, while the ablation experiment verified the effectiveness of this module. The experimental results on several public datasets demonstrated that our proposed framework is effective and superior to other existing similar detection networks.
KW - Face swapped images/videos
KW - Frequency clues
KW - Pixel-level classification
KW - Pseudo ground-truth generation
UR - https://www.scopus.com/pages/publications/85125593049
U2 - 10.1109/ICIP42928.2021.9506371
DO - 10.1109/ICIP42928.2021.9506371
M3 - Conference contribution
AN - SCOPUS:85125593049
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3597
EP - 3601
BT - 2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PB - IEEE Computer Society
T2 - 28th IEEE International Conference on Image Processing, ICIP 2021
Y2 - 19 September 2021 through 22 September 2021
ER -