Author:
Fatimah Alanazi
Affiliation:
School of Computing, Newcastle University, U.K. College of Computer Science and Engineering, University of Hafr Albatin, Saudi Arabia
Keyword(s):
Deepfakes Detection, Face Recognition, Image Analysis, Feature Fusion, Facial Features.
Abstract:
In the burgeoning era of deepfake technologies, the authenticity of digital media is being perpetually challenged, raising pivotal concerns regarding its veracity and the potential malicious uses of manipulated content. This study embarks on a meticulous exploration of the effectiveness of both internal and external facial features in discerning deepfake content. By conducting a thorough comparative analysis, our research illuminates the criticality of facial features, particularly those situated beyond the face’s center, in distinguishing between genuine and manipulated faces. The results elucidate that such features serve as potent indicators, thereby offering valuable insights for enhancing deepfake detection methodologies. Consequently, this research, therefore, not only underscores the paramount importance of these often-overlooked facial aspects but also contributes substantively to the domain of digital forensics, providing a nuanced understanding and innovative approaches tow
ards advancing deepfake detection strategies. By bridging the gap between technological advancements and ethical digital media practices, this study stands as a beacon, advocating for the imperative need to safeguard the integrity of digital communications in our progressively digitized world.
(More)