ABSTRACT With the rapid development of image editing technology, image manipulation localisation is facing increasingly severe challenges. Traditional methods cannot effectively capture tampering features at different scales and fail to fully utilise the tampering trace information at the boundary of the manipulated regions, resulting in limited localisation accuracy. To address these issues, we propose MEN‐IML, a multi‐scale edge‐aware network for image manipulation localisation. First, we design an efficient multi‐scale edge‐aware feature fusion module. This module enhances the expressive capability of edge details by assigning learnable weights to features at different scales and incorporating an edge feature awareness mechanism. Second, we adopt depthwise separable convolutional decoders instead of the traditional multi‐layer perceptron decoders, which not only focus on extracting spatial features of each channel, but also improve the model's ability to integrate cross‐channel information. Finally, we design a multi‐scale edge loss to supervise the boundaries of manipulated regions at multiple scales, effectively enhancing the sensitivity of the model to manipulated region boundaries. Cross‐dataset experimental results on multiple public datasets demonstrate that, compared to mainstream MVSS‐Net++ and the state‐of‐the‐art IML‐ViT, the proposed method improves average performance by 13.5% and 6.3%, effectively enhancing the localisation accuracy of image tampering.
Zhang et al. (2026) studied this question.