Purpose:
The objective of this project is to develop a Convolutional Neural Network (CNN) model for detecting image forgeries, differentiating between real and manipulated images. The project leverages Error Level Analysis (ELA) as a preprocessing step to enhance the discriminative features in the images. Methodology:
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Data Collection: • Utilize the CASIA dataset, containing authentic and tampered images(spliced and copy-moved images, as well as images affected by post-processing operations such as filtering and blurring), for training and evaluation. • Use ELA to preprocess images, enhancing the visual artifacts of manipulation.
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ELA Preprocessing: • Implement the ELA algorithm to highlight error levels in the images. • Adjust the ELA parameters such as quality and scaling for optimal results. • Integrate ELA preprocessing into the data pipeline.
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Model Architecture: • Design a CNN architecture for image forgery detection. • The CNN should take ELA-enhanced images as input. • Include convolutional layers, pooling layers, dense layers, and dropout layers for regularization.
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Data Augmentation: • Apply data augmentation techniques to artificially increase the size of the training dataset. • Augmentation may include rotation, flipping, and zooming.
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Model Training: • Train the CNN using the preprocessed images. • Use a binary classification setup with labels (real/fake). • Monitor training progress with validation data and implement early stopping.
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Model Evaluation: • Evaluate the model's performance on a separate validation set. • Visualize confusion matrices to understand model behavior.
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Hyperparameter Tuning: • Experiment with hyperparameter tuning to optimize model performance. • Adjust learning rates, batch sizes, and network architecture.
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Interpretability and Visualization: • Visualize feature maps and activations to understand what the model learns. • Use visualization tools to identify areas of interest in manipulated images.
