TY - GEN
T1 - CHIEFS
T2 - 15th International Symposium on Foundations and Practice of Security, FPS 2022
AU - Mohzary, Muhammad
AU - Almalki, Khalid
AU - Choi, Baek Young
AU - Song, Sejun
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - This paper presents a novel Machine Learning (ML)-based DeepFake detection technology named CHIEFS (Corneal-Specular Highlights Imaging for Enhancing Fake-Face Spotter). We focus on the most reflective area of a human face, the eyes, upon the hypothesis that the existing DeepFake creation methods fail to coordinate their counterfeits with the reflective components. In addition to the traditional checking of the reflection shape similarity (RSS), we detect various corneal-specular highlights features, such as color components and textures, to find corneal-specular highlights consistency (CHC). Furthermore, we inspect the ensemble of the highlights with the surrounding environmental factors (SEF), including the light settings, directions, and strength. We designed and built them as modular features and have conducted extensive experiments with different combinations of the components using various input parameters and Deep Neural Network (DNN) architectures on Generative Adversarial Network (GAN)-based DeepFake datasets. The empirical results show that CHIEFS with three modules improves the accuracy from 86.05% (with the RSS alone) to 99.00% with the ResNet-50-V2 architecture.
AB - This paper presents a novel Machine Learning (ML)-based DeepFake detection technology named CHIEFS (Corneal-Specular Highlights Imaging for Enhancing Fake-Face Spotter). We focus on the most reflective area of a human face, the eyes, upon the hypothesis that the existing DeepFake creation methods fail to coordinate their counterfeits with the reflective components. In addition to the traditional checking of the reflection shape similarity (RSS), we detect various corneal-specular highlights features, such as color components and textures, to find corneal-specular highlights consistency (CHC). Furthermore, we inspect the ensemble of the highlights with the surrounding environmental factors (SEF), including the light settings, directions, and strength. We designed and built them as modular features and have conducted extensive experiments with different combinations of the components using various input parameters and Deep Neural Network (DNN) architectures on Generative Adversarial Network (GAN)-based DeepFake datasets. The empirical results show that CHIEFS with three modules improves the accuracy from 86.05% (with the RSS alone) to 99.00% with the ResNet-50-V2 architecture.
KW - Corneal-Specular Highlights
KW - DeepFake
KW - DeepFake Detection
KW - Digital Media Forensics
KW - Media Manipulation
UR - http://www.scopus.com/inward/record.url?scp=85152585193&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85152585193&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-30122-3_10
DO - 10.1007/978-3-031-30122-3_10
M3 - Conference contribution
AN - SCOPUS:85152585193
SN - 9783031301216
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 158
EP - 172
BT - Foundations and Practice of Security - 15th International Symposium, FPS 2022, Revised Selected Papers
A2 - Jourdan, Guy-Vincent
A2 - Mounier, Laurent
A2 - Adams, Carlisle
A2 - Sèdes, Florence
A2 - Garcia-Alfaro, Joaquin
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 12 December 2022 through 14 December 2022
ER -