TY - GEN
T1 - Classifying Genuine Face images from Disguised Face Images
AU - Kim, Junyaup
AU - Han, Siho
AU - Woo, Simon S.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Detecting fake or disguised face images become much more challenging due to the significant advancements made in machine learning, computer vision, and image processing techniques. In addition, due to the rise of various DeepFakes, fake images can be maliciously used to attack individuals and deter true information. Therefore, it is crucial to building a classifier that accurately distinguishes an individual from different or similar persons. In this preliminary work, we aim to detect a target person's face from different similar individuals, Doppelgangers, leveraging the dataset from Disguised Faces in the Wild (DFW) 2018. We use well-known off-the-shelf face detection classifiers, such as ShallowNet, VGG-16, and Xception to evaluate the classification performance. In order to further improve the detection performance, we apply data augmentation. Our preliminary result shows that the Xception model can classify one from different individuals with a 62% accuracy.
AB - Detecting fake or disguised face images become much more challenging due to the significant advancements made in machine learning, computer vision, and image processing techniques. In addition, due to the rise of various DeepFakes, fake images can be maliciously used to attack individuals and deter true information. Therefore, it is crucial to building a classifier that accurately distinguishes an individual from different or similar persons. In this preliminary work, we aim to detect a target person's face from different similar individuals, Doppelgangers, leveraging the dataset from Disguised Faces in the Wild (DFW) 2018. We use well-known off-the-shelf face detection classifiers, such as ShallowNet, VGG-16, and Xception to evaluate the classification performance. In order to further improve the detection performance, we apply data augmentation. Our preliminary result shows that the Xception model can classify one from different individuals with a 62% accuracy.
KW - DeepFakes
KW - Disguised Face in the Wild (DFW)
KW - Doppelganger
KW - Fake Image Detection
UR - https://www.scopus.com/pages/publications/85081413348
U2 - 10.1109/BigData47090.2019.9005683
DO - 10.1109/BigData47090.2019.9005683
M3 - Conference contribution
AN - SCOPUS:85081413348
T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
SP - 6248
EP - 6250
BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
A2 - Baru, Chaitanya
A2 - Huan, Jun
A2 - Khan, Latifur
A2 - Hu, Xiaohua Tony
A2 - Ak, Ronay
A2 - Tian, Yuanyuan
A2 - Barga, Roger
A2 - Zaniolo, Carlo
A2 - Lee, Kisung
A2 - Ye, Yanfang Fanny
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Big Data, Big Data 2019
Y2 - 9 December 2019 through 12 December 2019
ER -