Back to Search
Start Over
DETECTION OF DEEP FAKES USING DEEP LEARNING.
- Source :
- I-Manager's Journal on Image Processing; Mar2024, Vol. 11 Issue 1, p1-11, 11p
- Publication Year :
- 2024
-
Abstract
- Deep learning algorithms have simplified the process of creating indistinguishable synthetic videos, or deep fakes, because of the unparalleled increase in processing power. It is concerning because these face-swapped manipulations are often used in a variety of contexts, such as blackmail and political manipulation. This paper presents a revolutionary deep learning-based approach to accurately discriminating between real and Artificial Intelligence (AI)-generated false films. Using a ResNext Convolutional Neural Network (CNN) for frame-level feature extraction, this method makes use of an automated mechanism intended to identify replacement and re-enactment deep fakes. A Recurrent Neural Network (RNN) equipped with Long Short-Term Memory (LSTM) training is utilized to classify videos and distinguish between real and modified ones. The system demonstrates the effectiveness of a straightforward and reliable methodology, in addition to utilizing complex neural network topologies. Through testing, this paper showcases how well the system can accurately identify videos playing a crucial role in ongoing initiatives to combat the increasing dangers posed by the proliferation of deep fake content in society. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23494530
- Volume :
- 11
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- I-Manager's Journal on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 177586898
- Full Text :
- https://doi.org/10.26634/jip.11.1.20719