A Deep Learning Based Deepfake AI (Images & Videos) Detection Tool

Mani Chourasiya¹ , Chetan Khapedia² , Diksha Bharawa³

Section:Research Paper, Product Type: Journal
Vol.12 , Issue.4 , pp.13-20, Aug-2024

Online published on Aug 31, 2024

Copyright © Mani Chourasiya, Chetan Khapedia, Diksha Bharawa . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Lenscan.ai represents a pivotal advancement in countering the rising threat posed by deepfake technology. This state-of-the-art AI tool integrates sophisticated computer vision and audio analysis algorithms to detect anomalies that signal deepfake manipulation in digital media. By scrutinizing visual indicators such as facial expressions and lip movements, coupled with auditory features like voice characteristics, Lenscan.ai employs a comprehensive, multi-modal approach to accurately identify falsified content. Its versatility extends across diverse media formats and platforms, playing a crucial role in mitigating risks across journalism, entertainment, and national security sectors. As deepfake methods become increasingly sophisticated, Lenscan.ai continues to evolve, ensuring it remains at the forefront of safeguarding the integrity and reliability of digital content. By doing so, it addresses the urgent need to combat misinformation, thereby preserving trust in the digital landscape and upholding the authenticity of information shared globally.

Key-Words / Index Term :
Deep Neural Networks, ,Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GANs). Autoencoders

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