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[
{
"Name": "advanced_cpmv",
"Title": "Advanced Detection of Copy-Move Forgery: Using Generative and Transformer-based Models for Distinguishing Source and Target Regions in Medical and Digital Imaging",
"Experiment": "Utilize a CNN-Transformer Generative Adversarial Network (GAN) that combines CNN's local feature extraction with the transformer's global context recognition. This setup will allow the model to generate masks that accurately distinguish source and target regions in forgery, aiming for higher precision in both localization and detection accuracy for digital and medical images.",
"Interestingness": 7,
"Feasibility": 4,
"Novelty": 3
},
{
"Name": "deep_cpmv",
"Title": "Frontiers in Copy-Move Forgery Detection: A Comprehensive Survey of Deep Learning Innovations for Digital and Medical Image Integrity",
"Experiment": "Conduct an extensive literature review to compile and synthesize recent advancements, particularly in GANs, CNN-Transformer models, and feature fusion techniques for copy-move forgery detection. This survey aims to highlight the evolution from traditional SVM-based techniques to the latest deep learning-driven approaches that improve accuracy and robustness in diverse imaging contexts.",
"Interestingness": 7,
"Feasibility": 4,
"Novelty": 3
}
]
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