• Miss. Sakshi Kishor Aher
• Mr. H.M. Gaikwad
• Mrs. J.S.Mahajan
• Miss Namrata Ramkrishna Parkhe
• Miss Anushka Anil Pagare

This project develops a deepfake detection system using CNN (ResNext) and RNN (LSTM) to analyze video frames and detect manipulations. It addresses challenges posed by advanced face-swapping techniques by identifying inconsistencies in lighting, reflections, and facial details. The model is trained on a diverse dataset to improve accuracy and adaptability. With a scalable and interpretable approach, this system helps combat deepfake threats and ensures the authenticity of digital content.

Deepfake Detection, Face-Swap Detection, Deep Learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Video Frame Analysis, Fake Video Detection, AI-Based Video Forensics

"Detection of face swap based deep fake videos", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.i691-i702, March-2025, Available :http://www.jetir.org/papers/JETIR2503894.pdf

"Detection of face swap based deep fake videos", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppi691-i702, March-2025, Available at : http://www.jetir.org/papers/JETIR2503894.pdf