Seeing Through the Fake: Explainable AI With Multiple CNNs for Deepfake Detection
TL;DR
This study addresses deepfake detection challenges by exploring model configurations and training strategies using multiple CNNs and explainable AI to enhance the credibility of digital content.
Seeing Through the Fake: Explainable AI With Multiple CNNs for Deepfake Detection
Muhammad Aleem; Muhammad Umair; Muhammad Zubair; Rozeena Ibrahim; Muhammad Tahir Naseem; Muhammad Mohsin Raza; Muhammad Nadeem Ali; Byung-Seo Kim
https://doi.org/10.1109/ACCESS.2025.3649128
Volume 14
The rapid advancement of deepfake generation techniques poses significant challenges to the credibility of digital content by producing highly realistic manipulated content. This study explores two primary scenarios: the first is model configuration, and the second is training strategies. In the model configuration scenario, we combine deep learning-based feature extraction with machine learning-b...