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Comparative Analysis of Deepfake Image Detection Method Using Convolutional Neural Network.

Hasin Shahed Shad1, Md Mashfiq Rizvee1, Nishat Tasnim Roza1

  • 1Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh.

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This study compares eight convolutional neural network (CNN) models for deepfake image detection. The VGGFace model achieved the highest accuracy at 99%, demonstrating its effectiveness in distinguishing real images from AI-generated deepfakes.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deepfake technology, an AI-driven method for face swapping, is increasingly prevalent on social media.
  • The rapid advancement of deepfakes makes distinguishing synthesized images from real ones challenging.
  • This poses a growing concern due to the potential for misuse.

Purpose of the Study:

  • To accurately detect deepfake images from real ones.
  • To implement and comparatively analyze various deepfake detection methods.
  • To evaluate the performance of different convolutional neural network (CNN) architectures for this task.

Main Methods:

  • Trained eight distinct CNN models on a dataset of 140,000 images (70,000 real from Flickr, 70,000 deepfakes from StyleGAN).
  • Included DenseNet (121, 169, 201), VGGNet (16, 19), ResNet50, VGGFace, and a custom CNN architecture.
  • Evaluated models using accuracy, precision, recall, F1-score, and area under the ROC curve.

Main Results:

  • The VGGFace model achieved the highest accuracy at 99%.
  • ResNet50 and DenseNet121 models showed strong performance with 97% accuracy.
  • Other models like DenseNet201 (96%), DenseNet169 (95%), VGG19 (94%), VGG16 (92%), and the custom model (90%) also provided significant detection capabilities.

Conclusions:

  • Comparative analysis highlights VGGFace as the top-performing model for deepfake image detection.
  • CNNs, particularly deep learning architectures, are effective tools for identifying sophisticated AI-generated imagery.
  • Continued research and development in deepfake detection are crucial to mitigate associated risks.