Real vs. AI-Generated Image Detection Model
This repository contains a deep learning model trained to classify images as either real or AI-generated (fake). The model was developed as part of a project on Big Data and leverages transfer learning with the powerful ResNet50 architecture. It achieves a test accuracy of 93.34% on the CIFAKE dataset. 🏆
Model Architecture
The model uses a transfer learning approach, which involves two main components:
Base Model: A pre-trained ResNet50 model, with its weights trained on the ImageNet dataset, is used as a feature extractor. Its layers were frozen during training to retain the learned features.
Custom Classifier Head: A custom head was added on top of the base model to perform the final binary classification. It consists of:
A
Flatten layer.
A
Dense layer with 512 units and a ReLU activation function.
A
Dropout layer with a rate of 0.5 to prevent overfitting.
A final
Dense output layer with a Sigmoid activation function for binary classification.
Dataset
The model was trained on the
CIFAKE dataset, which is available on Kaggle. This dataset contains a large collection of images, split into two classes:
REAL: Authentic, real-world photographs.
FAKE: Images synthetically generated by AI models.
To improve the model's ability to generalize and to prevent overfitting,
data augmentation techniques were applied to the training set, including random flips, rotations, and zooms.
Training & Performance
Training Environment: The model was trained on Google Colab using an NVIDIA T4 GPU.
Training Process: The model was trained for 20 epochs using the Adam optimizer and binary_crossentropy loss function.
ModelCheckpoint was used to save the best weights after each epoch.
Final Performance: The final model was evaluated on an unseen test set and achieved the following results:
Test Accuracy: 93.34%
Precision (for REAL class): 95%
Recall (for FAKE class): 95%
The model's performance slightly exceeds the 93% accuracy reported in the dataset's base paper, demonstrating its effectiveness.
Confusion Matrix
The confusion matrix below shows a detailed breakdown of the model's performance on the 20,000 images in the test set.
