Enhanced Deepfake Detection Using Resnet50 And Facial Landmark Analysis

Abstract

This Research Focuses On Accuracy Enhancement In The Detection Of Deepfakes Using The ResNet50 Algorithm Designed Through Deep Learning. It Analyzes Anomalies In Artificial Facial Images. Materials And Methods: The Two Implemented Deep Learning Models Include MobileNetV2 (Group 1) And ResNet50 (Group 2), Each Trained And Tested With 40 Image Samples, Comprising 20 Real Images And 20 Deepfake Images. Here, A Facial Irregularity Detector Based On ResNet50 Was Trained Against One Whose Model Was Created Through MobileNetV2. Result: ResNet50 Was Shown To Have A Detection Accuracy Of 91.81 % To 97.87 % For Distinguishing Between Real And Fake Photographs. Its Effectiveness For Real-time Applications Is Demonstrated. Statistical Study Revealed A Significant Improvement In Detection Accuracy Than The MobileNetV2 Model (p-value < 0.05). Conclusion: According To The Study's Results, The ResNet50 Algorithm Is Very Good At Identifying Deepfake Photos And Real Photos With A Low Mistake Rate And High Accuracy. Due To Its Efficiency In Processing Synthetic And Genuine Images, It Can Be A Dependable Tool For Handling The Problems Created By Deepfake Media.

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