Laplacian Pyramid Reweighting With Progressive Residual Learning for Image Forgery Localization

Abstract

The increasing realism of image manipulations poses significant challenges for forgery localization. However, existing methods are hindered by the limited adaptability of constrained frequency filters and the dilution of subtle forensic cues in deep networks. To address these challenges, we propose the Laplacian pyramid reweighting with progressive Residual Learning framework (LapRL-Net). First, a Laplacian Residual Adaptive Reweighting (LRAR) module is introduced to adaptively modulate multi-scale frequency residuals, enabling flexible extraction of discriminative frequency artifacts. Second, to mitigate feature dilution, we design a Progressive global-local Residual Fusion Module (PRFM) with multi-level residual fusion, which progressively combines global contextual dependencies with local texture details to preserve critical forensic cues. Furthermore, an Edge-Guided Refinement Module (EGRM) is incorporated to enhance boundary accuracy by enforcing geometric consistency via edge supervision. Extensive experiments on multiple benchmarks demonstrate that the proposed method achieves competitive performance in complex forensic scenarios.