Using cascade CNN-LSTM-FCNs to identify AI-altered video based on eye state sequence.

Autor: Saealal MS; Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan Campus, Pekan, Pahang, Malaysia.; Electrical Engineering Technology Department, Faculty of Electric and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia., Ibrahim MZ; Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan Campus, Pekan, Pahang, Malaysia., Mulvaney DJ; School of Electronic, Electrical and Systems Engineering, Loughborough University, Loughborough, United Kingdom., Shapiai MI; Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institue of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia., Fadilah N; Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan Campus, Pekan, Pahang, Malaysia.

Publikováno v: PloS one [PLoS One] 2022 Dec 15; Vol. 17 (12), pp. e0278989. Date of Electronic Publication: 2022 Dec 15 (Print Publication: 2022).