Naive CNN Model for Identifying AI Generated Faces

A very simplistic approach to a CNN to verify if an image of a human face is AI generated or not. Special attention is given to training on high quality images and testing high quality images.

Warning

This model is not working yet. It needs pre-processing for images or other contexts.

About the Model

This model has 2 parameters: real or synth. It is trained on the following:

• FFHQ: real faces – https://github.com/NVlabs/ffhq-dataset
• SFHQ: synthetic faces – https://github.com/SelfishGene/SFHQ-dataset

This is a total of >160,000 images.

Structure

Note

Model training data is not synced, but a pre-trained model is.

• saves – contains the saved trained keras model.
• reports – results of previous runs and testing.
• predict – the images used to test the model.
• predict_modified – modified versions of testing images (modifications made as a pre-process step to testing).
• get_predict.py – the runner for testing predefined images.

To Use

1. Sync repository
2. Install Python dependencies
3. Use the run notebook to run tests.
4. Review stats notebook

Model Summary

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 1022, 1022, 64)    1792      
                                                                 
 max_pooling2d (MaxPooling2  (None, 511, 511, 64)      0         
 D)                                                              
                                                                 
 conv2d_1 (Conv2D)           (None, 509, 509, 128)     73856     
                                                                 
 max_pooling2d_1 (MaxPoolin  (None, 254, 254, 128)     0         
 g2D)                                                            
                                                                 
 conv2d_2 (Conv2D)           (None, 252, 252, 256)     295168    
                                                                 
 max_pooling2d_2 (MaxPoolin  (None, 126, 126, 256)     0         
 g2D)                                                            
                                                                 
 conv2d_3 (Conv2D)           (None, 124, 124, 512)     1180160   
                                                                 
 global_average_pooling2d (  (None, 512)               0         
 GlobalAveragePooling2D)                                         
                                                                 
 dense (Dense)               (None, 1)                 513       
...
Total params: 1551489 (5.92 MB)
Trainable params: 1551489 (5.92 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________