Year: 2025
Type: article
Abstract: In order to identify deep fakes and other forms of altered facial information, this work details the development and implementation of a face forgery detection system. We propose a system that recognizes subtle changes in face images and videos using state-of-the-art machine learning techniques. After being trained on publically available datasets, the system is evaluated using key performance metrics such as accuracy, precision, and recall. To construct the system, convolutional neural networks, or CNNs, were used. The tests are carried out using publicly available datasets. In order to make it a robust model, a custom dataset is also built. We also look at how this technology could be used to secure digital identities and combat misinformation, opening the door for future collaboration with global cybersecurity and digital safety initiatives.
Authors: Chandani -, Saumya Pathak -, Nikhil Kumar -, Nancy Agarwal -
Institution: Galgotias University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407268041