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    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 Leve...

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