A Comparative Evaluation of Local Feature Descriptors for DeepFakes Detection
The global proliferation of affordable photographing devices and readily-available face image and video editing software has caused a remarkable rise in face manipulations, e.g., altering face skin color using FaceApp. Such synthetic manipulations are becoming a very perilous problem, as altered faces not only can fool human experts but also have detrimental consequences on automated face identification systems (AFIS). Thus, it is vital to formulate techniques to improve the robustness of AFIS against digital face manipulations. The most prominent countermeasure is face manipulation detection, which aims at discriminating genuine samples from manipulated ones. Over the years, analysis of microtextural features using local image descriptors has been successfully used in various applications owing to their flexibility, computational simplicity, and performances. Therefore, in this paper, we study the possibility of identifying manipulated faces via local feature descriptors. The comparative experimental investigation of ten local feature descriptors on a new and publicly available DeepfakeTIMIT database is reported.
2019 IEEE International Symposium on Technologies for Homeland Security, HST 2019
Akhtar, Z., & Dasgupta, D. (2019). A Comparative Evaluation of Local Feature Descriptors for DeepFakes Detection. 2019 IEEE International Symposium on Technologies for Homeland Security, HST 2019 https://doi.org/10.1109/HST47167.2019.9033005