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This paper presents a novel deep fake face detection system utilizing machine learning. The system employs Convolutional Neural Networks (CNNs) to analyze subtle facial artifacts and motion inconsistencies. It aims to accurately differentiate between real and synthetic facial videos. A comprehensive dataset is used for training, enhancing detection accuracy. The goal is to combat the spread of misinformation and digital forgery. This research contributes to the development of ethical AI solutions for digital content verification. It addresses the growing threat posed by deep fake technology in various domains.
Keywords:
Deep fake, Face Detection, Deep Learning, GANs, Computer Vision, CNNs, Image Forensics, Fake Video Detection, Feature Extraction, Frame Analysis, Synthetic Media, Video Manipulation, Digital Forgery, Misinformation Prevention, Dataset Training, Model Classification, Real vs Fake Identification, AI Ethics, Facial Landmark Analysis.
Cite Article:
"Deep Fake Face Detection", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a414-a423, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504060.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator