Deep Fake Image/Video detection Using Deep Learning

Abstract

The proliferation of deepfake technology, which uses
artificial intelligence to create highly realistic synthetic
videos and images, poses significant threats to privacy,
security, and trust in digital media. Traditional methods
for detecting these manipulations often fall short due to
the sophisticated nature of deepfake algorithms. This
paper proposes a novel approach for deepfake face
detection using Deep Learning (DL) well-suited for
sequential data analysis. Our method leverages the
temporal dependencies and patterns inherent in video
sequences to identify subtle inconsistencies and
artifacts introduced by deepfake generation processes.
By analyzing frames in a sequence rather than in
isolation, the DL can capture dynamic facial features
and movements that are difficult to replicate accurately
in deepfakes. The proposed model is trained on a
comprehensive dataset of real and deepfake videos,
incorporating various scenarios and levels of
manipulation. Experimental results demonstrate that
our DL-based approach achieves superior accuracy and
robustness compared to state-of-the-art deepfake
detection techniques, particularly in challenging cases
with high-quality deepfakes. Furthermore, the model
exhibits strong generalization capabilities across
different datasets and deepfake generation methods.
This research highlights the potential of DL for
enhancing the detection of deepfake content,
contributing to the development of more secure and
trustworthy digital media platforms.