Fake Image Detection Using Deep Learning

· ongoing · computer-vision deep-learning research

Objective

Develop a deep learning model to detect AI-generated fake images with high accuracy.

Approach

  • Using CNN architecture (ResNet50 as baseline)
  • Dataset: Mix of real images and AI-generated (DALL-E, Midjourney)
  • Training on 10k images, validation on 2k

Current Results

  • Baseline accuracy: 78%
  • False positive rate: 12%

Next Steps

  • Try transfer learning with EfficientNet
  • Augment dataset with more diverse fake images
  • Implement attention mechanisms

Notes

The model struggles with high-quality AI generations. Need to focus on subtle artifacts.