Abstract
Deepfake technology has rapidly evolved, enabling the creation of highly convincing synthetic media, including images, videos, and audio recordings. These maliciously manipulated digital artifacts can have severe consequences for privacy, security, and trust in digital content. Therefore, the development of effective deepfake detection methods is of paramount importance. This paper proposes a novel approach for deepfake detection based on the analysis of human eye blinking patterns. The human eye blink is a unique and subtle behavioral trait that is challenging to replicate accurately in deepfake videos. We leverage this inherent biometric feature to identify anomalies in video content and distinguish between genuine and synthetic videos. Key Words: Eye Blink to Speech, Machine Learning.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
