Deepfake detection analyzes digital images to classify them as real or synthetically generated. A practical implementation fine-tunes a pretrained ResNet50 instead of learning visual features from scratch, adapting its final layers to binary classification. section 13.3 section 13.5
Build the detector through these stages:
- Collect diverse, clearly labeled real and fake images, then divide them into training, validation, and test subsets. section 13.4
- Inspect representative examples; convincing fake faces demonstrate why visual judgment alone is insufficient. section 13.4.1
- Apply ResNet50 preprocessing: resize and crop images, convert them to tensors, normalize them with ImageNet statistics, and batch them for training. section 13.4.1
- Freeze early ResNet50 layers, replace the output head, train the new layers, monitor validation performance, and use early stopping to limit overfitting. section 13.4
- Evaluate predictions across the test set and inspect images labeled with both ground-truth and predicted classes. section 13.4.3
Fine-tuned face-image classifier
Using the 140k Real and Fake Faces dataset, the workflow loads 50,000 real and 50,000 fake training images plus balanced validation and test subsets. The resulting fine-tuned ResNet50 classifies 93.73% of the 20,000 test images correctly. section 13.4.1 section 13.4.3
Listing 13.4 Downloading the 140k Real and Fake Faces dataset
import kagglehub
path = kagglehub.dataset_download(
"xhlulu/140k-real-and-fake-faces") #1
print("Path to dataset files:", path) #2
train_dir=rf"{path}/real_vs_fake/real-vs-fake/train"
val_dir=rf"{path}/real_vs_fake/real-vs-fake/valid"
test_dir=rf"{path}/real_vs_fake/real-vs-fake/test" #3
Use the dataset-download listing to reproduce the directory layout expected by the image-loading and classification pipeline. section 13.4.1
