International Journal on Science and Technology

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Deep-Fake Detection Using Deep Learning

Author(s) Piyusha Siripurapu, Valluru Lakshmi chandrika, Dhanasree Prattipati, Gudapati Sai Manoj, Mrs P Sarala
Country India
Abstract In recent years, the rise of deepfakes-synthetic media generated using artificial intelligence has raised seriousconcerns due to its potential misuse in various fields such as politics, entertainment, and cybercrime. This project, titled"Deepfake Detection Using Deep Learning," aims to develop a robust system for identifying and classifying deepfakecontent. The proposed approach leverages advanced deep learning techniques, including Convolutional NeuralNetworks (CNNs) and Recurrent Neural Networks (RNNs), to detect inconsistencies and temporal patterns.Additionally, Generative Adversarial Networks (GANs) play a key role, with StyleGAN employed for generating high-quality fake images and CycleGAN for domain adaptation. The deepfake detection model is trained on a diversedataset of real and manipulated content, with the goal of improving the accuracy and generalization capability of thesystem. By combining the power of CNNs for image analysis, RNNs for sequential data processing, and GANs forunderstanding the nature of fake content generation, this project provides a comprehensive solution to the growingthreat posed by deepfakes.
Keywords Deepfake, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs),StyleGAN, CycleGAN, Facial Recognition, Multimedia Manipulation, Facial Feature Analysis.
Field Engineering
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-16
Cite This Deep-Fake Detection Using Deep Learning - Piyusha Siripurapu, Valluru Lakshmi chandrika, Dhanasree Prattipati, Gudapati Sai Manoj, Mrs P Sarala - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2476
DOI https://doi.org/10.71097/IJSAT.v16.i1.2476
Short DOI https://doi.org/g88r94
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