Advanced Copy-Move Forgery Detection Solution

1. Copy-Move Forgery Detection
Challenges:
 Post-processing transformations (e.g., rotation, scaling, flipping).
 Limited generalization to diverse datasets.
 Neglect of cross-image forgeries.
Proposed Solutions:
1. Feature Extraction Enhancement:
o Use deep learning models (e.g., CNNs with attention mechanisms) to detect
transformation-invariant features.
o Integrate transformer models for better spatial and contextual understanding.
2. Cross-Domain Learning:
o Employ unsupervised domain adaptation techniques to enhance generalization
across datasets.
o Augment training datasets with synthetic forgery samples using Generative
Adversarial Networks (GANs).
3. Multi-Modal Approaches:
o Combine image metadata (EXIF) with visual features to improve detection in
cross-image scenarios.
o Leverage multi-branch CNNs for source-target disambiguation.
Novelty:
 A hybrid model combining convolutional and transformer architectures for detecting
transformations in copy-move forgery.
 Augmented datasets and domain adaptation pipelines for robust model generalization