Deepfake Video Detection using Machine Learning

Deepfake Video Detection using Machine Learning

Aditya Dumbre1, Omsai Alladwar2, Venktesh Kale3, Dr. P. A. Kadam4

 1,2,3Student, 4Professor

1,2,3,4Department of Computer Engineering

1,2,3,4 Smt. Kashibai Navale College of Engineering, Pune, Maharashtra, India

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Abstract - The rise of DeepFake (DF) technology poses a significant threat to the authenticity and reliability of digital media. While such videos once required expert skills and high-end software, the recent advancement of deep learning tools has made the generation of hyper-realistic synthetic media widely accessible. This paper presents a robust and efficient deep learning-based solution for detecting DeepFake videos by combining spatial and temporal analysis using a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN). Our final system leverages a ResNeXt50 CNN for frame-level feature extraction and an LSTM layer to model temporal inconsistencies across sequential video frames. Extensive experiments conducted on a balanced dataset—comprising both real and manipulated videos sourced from FaceForensics++, YouTube, and the DeepFake Detection Challenge dataset—demonstrate the high accuracy and reliability of our model. Additionally, the developed solution is deployed as a web-based platform allowing real-time DeepFake video detection with user-friendly interaction. This paper outlines the design, implementation, results, and scope of our complete DeepFake detection system.

Keywords: DeepFake Detection, CNN, LSTM, ResNeXt, Temporal Analysis, Video Forensics, Media Authenticity, Deep Learning, Media Verification, Face Forensics