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DeepFake Detection Using MobileNetV2 and LSTM

  • Unique Paper ID: 175349
  • Volume: 11
  • Issue: 11
  • PageNo: 3080-3086
  • Abstract:
  • The spread of DeepFake technology threatens digital media integrity to a large extent, calling for effective detection methods. This paper introduces a DeepFake detection system that uses MobileNetV2 for spatial feature extraction, LSTM for temporal analysis, and MTCNN for face detection, with a test accuracy of 95%. We trained the model on the Celeb-DF dataset, which comprises 199 videos (99 real, 100 fake), with 5 frames per video to strike a balance between computational efficiency and detection accuracy. We improved the performance of the model by iterative threshold optimization, increasing accuracy from 85.71% (threshold 0.5) to 95% (threshold 0.18). Our approach involves fine-tuning MobileNetV2 with the addition of temporal analysis using LSTM and optimal thresholding of classification to trade-off between false positives and false negatives. Our experiments showcase the efficacy of our method at detecting nuanced DeepFakes while preserving a high accuracy on natural videos, thus rendering it an effective solution for practical applications.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 3080-3086

DeepFake Detection Using MobileNetV2 and LSTM

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