<p>The widespread use of manipulated and synthetic images in cybercrime, identity fraud, and misinformation campaigns has created serious threats to digital trust and multimedia security systems. As image manipulation techniques continue to advance, reliable localization of tampered regions has become a critical requirement for digital forensic analysis. Existing image manipulation localization approaches, including handcrafted feature-based methods and convolutional neural networks (CNNs), often suffer from limited flexibility or insufficient sensitivity to subtle forensic artifacts. CNN-based models may overlook fine-grained traces, while handcrafted approaches rely on rigid assumptions that hinder generalization. To address these limitations, this paper proposes a multi-branch hierarchical framework with four core innovations: (1) a dual-branch architecture processing noise residuals (Bayar and SRM convolutions) and frequency-domain features (DCT-based decomposition) in parallel; (2) a noise–frequency feature fusion strategy within a separate encoder–decoder backbone; (3) a dual attention mechanism capturing long-range spatial and cross-channel dependencies; and (4) a hierarchical encoder–decoder design with skip connections for precise pixel-level localization. Experimental evaluations on benchmark forensic datasets, including CASIA, COVERAGE, COLUMBIA, and NIST16, demonstrate that the proposed framework achieves competitive detection performance and robustness across most datasets, attaining an AUC of up to 0.998 on NIST16, while highlighting specific failure modes on challenging cases such as COVERAGE. The proposed system can be integrated into digital forensic pipelines and security platforms to support proactive detection of counterfeit image attacks.</p>