A Survey Paper on LSTM-Based Approach for DeepFake Video Detection

This research studies the use of Long Short-Term Memory (LSTM) networks for detecting DeepFake videos. DeepFake technology presents serious problems for preserving the authenticity and integrity of digital media since it uses sophisticated deep learning techniques to alter video content. We created...

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Bibliographic Details
Published in:International journal for research in applied science and engineering technology 2024-11, Vol.12 (11), p.1464-1468
Main Authors: Kulkarni, Dr. Nikita, Desai, Aditya, Bhamare, Soham, Bamne, Prathmesh, Adawale, Vivek
Format: Article
Language:English
Online Access:Get full text
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Summary:This research studies the use of Long Short-Term Memory (LSTM) networks for detecting DeepFake videos. DeepFake technology presents serious problems for preserving the authenticity and integrity of digital media since it uses sophisticated deep learning techniques to alter video content. We created a model capable of accurately detecting synthetic media by leveraging LSTM networks' sequential processing capabilities. The study focuses on taking advantage of temporal irregularities that are frequently found in DeepFake films but are difficult to identify using static analysis.
ISSN:2321-9653
2321-9653
DOI:10.22214/ijraset.2024.65235