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Unmasking Reality : Advanced Deepfake Detection for Images and Videos

Author(s) Dr. S. Brindha, Ms. I. N. Sountharia, Mr. V. S. Thaneshvar, Mr. J. S. Jayanishanth, Mr. R. Rathivarman, Mr. R. Udaya Ganesh
Country India
Abstract Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, making it difficult to distinguish between real and fake features. This technology has greatly advanced, promoting a wide range of applications in cinema, such as improving visual effects in movies, as well as various criminal activities, such as misinformation generation by mimicking famous people. To identify and classify DeepFakes, research in DeepFake detection using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has attracted increased interest. Essentially, DeepFake is regenerated media obtained by injecting or replacing some information within CNN and RNN models. This paper summarizes the DeepFake detection methods for face images and videos based on their results, performance, methodology used, and detection type. The challenges in generating a generalized DeepFake detection model are also analyzed.
Keywords Deepfake, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Resnet50, Long Short Term Memory(LSTM).
Field Engineering
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-02-24
Cite This Unmasking Reality : Advanced Deepfake Detection for Images and Videos - Dr. S. Brindha, Ms. I. N. Sountharia, Mr. V. S. Thaneshvar, Mr. J. S. Jayanishanth, Mr. R. Rathivarman, Mr. R. Udaya Ganesh - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2063
DOI https://doi.org/10.71097/IJSAT.v16.i1.2063
Short DOI https://doi.org/g85946
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