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Findings confirm that the proposed MAFN-HFL is a scalable and efficient solution for next-generation image forgery detection, and shows strong performance on two benchmark datasets.
An innovative EDSP-QCH (Energy-efficient Dynamic Sub-Station Placement and reward-based Q-Learning reinforcement model for Cluster Head selection) strategy is proposed, which indicates significant improvements in energy efficiency and network robustness.
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