In-the-wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy

• Main Authors: Saealal, Muhammad Salihin, Ibrahim, Mohd Zamri, Shapiai, Mohd Ibrahim, Fadilah, Norasyikin
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Online Access: http://eprints.utem.edu.my/id/eprint/28039/1/In-the-wild%20deepfake%20detection%20using%20adaptable%20CNN%20models%20with%20visual%20class%20activation%20mapping%20for%20improved%20accuracy.pdf; http://eprints.utem.edu.my/id/eprint/28039/; https://ieeexplore.ieee.org/document/10210096
• Institution: Universiti Teknikal Malaysia Melaka

Deepfake technology has become increasingly sophisticated in recent years, making detecting fake images and videos challenging. This paper investigates the performance of adaptable convolutional neural network (CNN) models for detecting Deepfakes. In-the-wild OpenForensics dataset was used to e...