• Manumitha G P
• Rudresh N C
• Pragathi V
• Shreya S S
• Swetha B T

This study focuses on using machine learning to spot deepfake content, which are manipulative videos or images often created with malicious intent. We employ advanced computer algorithms that deepfake detection system using Convolutional Neural Networks (CNNs) using a collection of fictitious photos and videos is a critical endeavor in the face of increasing concerns about manipulated media content. To identify subtle differences in deepfakes that set them apart from real media, like unnatural facial movements or audio inconsistencies. Through extensive training and testing, our method proves effective at accurately detecting these deceptive creations, offering a valuable defense against the spread of misleading or harmful digital content. For example: Sometimes people make videos that look real, but they are not. We taught computers to see the mistakes in these fakes, like weird faces or voices. Our method works well at finding these tricky fakes, which helps keep the digital world truthful and safe.

Deepfake, Convolutional Neural Network (CNN), Manipulative images, Trans Net, Recurrent Neural Network (RNN)

"Deepfake Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.k436-k438, April-2024, Available :http://www.jetir.org/papers/JETIR2404A58.pdf

"Deepfake Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppk436-k438, April-2024, Available at : http://www.jetir.org/papers/JETIR2404A58.pdf