TY - GEN
T1 - Cross-Dataset Face Manipulation Detection
AU - Bekci, Burak
AU - Akhtar, Zahid
AU - Ekenel, Hazim Kemal
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/5
Y1 - 2020/10/5
N2 - Easily available recent face image/video manipulation techniques and tools are now being utilized to generate highly realistic manipulated videos known as DeepFakes, which can fool face recognition systems and humans. Thus, it is vital to devise precise manipulation detection methods. Despite the progress, existing mechanisms are limited to the datasets or manipulation types. In this paper, to increase the performance under unseen data and manipulations, a DeepFakes detection framework using metric learning and steganalysis rich models is presented. Extensive empirical analysis on three publicly available datasets, namely, FaceForensics++, CelebDF, and DeepFakeTIMIT, were carried out to evaluate the generalization capability of the proposed approach. The framework attained 5% to 15% accuracy gains under unseen manipulations.
AB - Easily available recent face image/video manipulation techniques and tools are now being utilized to generate highly realistic manipulated videos known as DeepFakes, which can fool face recognition systems and humans. Thus, it is vital to devise precise manipulation detection methods. Despite the progress, existing mechanisms are limited to the datasets or manipulation types. In this paper, to increase the performance under unseen data and manipulations, a DeepFakes detection framework using metric learning and steganalysis rich models is presented. Extensive empirical analysis on three publicly available datasets, namely, FaceForensics++, CelebDF, and DeepFakeTIMIT, were carried out to evaluate the generalization capability of the proposed approach. The framework attained 5% to 15% accuracy gains under unseen manipulations.
KW - Deep Learning
KW - DeepFake
KW - Face Manipulation
KW - Generalization
KW - Metric Learning
UR - http://www.scopus.com/inward/record.url?scp=85096607122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096607122&partnerID=8YFLogxK
U2 - 10.1109/SIU49456.2020.9302157
DO - 10.1109/SIU49456.2020.9302157
M3 - Conference contribution
AN - SCOPUS:85096607122
T3 - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
BT - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th Signal Processing and Communications Applications Conference, SIU 2020
Y2 - 5 October 2020 through 7 October 2020
ER -