license: apache-2.0
metrics:
- accuracy
- f1
base_model:
- google/vit-base-patch16-224-in21k
Checks whether the image is real or fake (AI-generated).
Note to users who want to use this model in production:
Beware that this model is trained on a dataset collected about 2 years ago. Since then, there is a remarkable progress in generating deepfake images with common AI tools, resulting in a significant concept drift. To mitigate that, I urge you to retrain the model using the latest available labeled data. As a quick-fix approach, simple reducing the threshold (say from default 0.5 to 0.1 or even 0.01) of labelling image as a fake may suffice. However, you will do that at your own risk, and retraining the model is the better way of handling the concept drift.
See https://www.kaggle.com/code/dima806/cifake-ai-generated-image-detection-vit for more details.
Classification report:
precision recall f1-score support
REAL 0.9868 0.9780 0.9824 24000
FAKE 0.9782 0.9870 0.9826 24000
accuracy 0.9825 48000
macro avg 0.9825 0.9825 0.9825 48000
weighted avg 0.9825 0.9825 0.9825 48000