DeepFake Detection using YOLOv8:Advanced Image – Based Detection Techniques
Chubaranjani S
, Aswitha A
Detection of DeepFakes, YOLOv8 Model, Machine Learning Approaches,Real-time Analysis.
Abstract—"The proliferation of DeepFake technology has raised critical concerns in areas such as misinformation, security, and privacy by enabling the creation of highly realistic but misleading media. In response, this study introduces a method for detecting DeepFake images using the advanced object detection capabilities of the YOLOv8 model. By training YOLOv8 on a tailored dataset of authentic and manipulated images, and employing various pre-processing and data augmentation techniques, we aim to enhance the accuracy and robustness of detection. Our model achieved an accuracy of 73.9%, illustrating its potential for effective and scalable DeepFake detection. Further improvements, including the integration of multi-modal detection approaches such as video and behavioral analysis, are proposed to bolster the system's adaptability to evolving threats."
"DeepFake Detection using YOLOv8:Advanced Image – Based Detection Techniques", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.12, Issue 4, page no.a334-a338, April-2025, Available :https://tijer.org/TIJER/papers/TIJER2504054.pdf
Volume 12
Issue 4,
April-2025
Pages : a334-a338
Paper Reg. ID: TIJER_157122
Published Paper Id: TIJER2504054
Downloads: 000304
Research Area: Science and Technology
Country: Thiruvallur, Tamil Nadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
