Deep Fake Detection

Deep Fake Detection

Mr. SANJAY M 1, P B SANDEEP 2, RACHANA G C 3, SOORYA PRAKASH S 4, SRUJAN S R5

1 Assistant. Professor, Dept. of Information Science & Engineering, Rajeev Institute of Technology, Hassan

2 Information Science & Engineering, Rajeev Institute of Technology, Hassan 3 Information Science & Engineering, Rajeev Institute of Technology, Hassan 4 Information Science & Engineering, Rajeev Institute of Technology, Hassan 5 Information Science & Engineering, Rajeev Institute of Technology, Hassan

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Abstract - Authenticity of Smart Media A method called Deep Fake identification With Machine Learning uses deep learning approaches to enhance the identification of AI-manipulated media. Artificial intelligence (AI) produces incredibly lifelike synthetic movies known as "deep fakes," which can cause political instability, disinformation, and harm to one's reputation. This project uses preprocessing methods like face cropping and frame extraction to analyse video material. While LSTM is used for temporal sequence modelling to categorise movies as real or deepfake, ResNeXt CNN is employed for feature extraction. Real-time detection and increased accuracy are the outcomes of the system's automation of video forensics. It guarantees dependable results and offers users an easy-to-use online interface by utilising deep learning.

Key words: LSTM, deepfake detection, facial recognition, video forensics, computer vision, deep learning, ResNeXt, and media authenticity.