Progressive Mask Transformer With Edge Enhancement for Image Manipulation Localization

Ye Zhu, Jian Liu, Yang Yu, Yingchun Guo, Xiaoke Hao

Abstract: Recent developments in image editing techniques have given rise to serious challenges to the credibility of multimedia data. Although some deep learning methods have achieved impressive results, they often fail to detect subtle edge artefacts, and current mainstream methods focus mainly on the foreground content and ignore the background content, which also contains abundant information related to manipulation. To address this issue, this letter proposes a progressive mask transformer with an edge enhancement network for image manipulation localization. Specifically, an edge enhancement flow is introduced to detect subtle manipulated edge artefacts and guide the localization of manipulated regions. Then, the manipulated, genuine and global features are progressively refined using a progressive mask transformer module. We perform extensive experiments on NIST16, Coverage, CASIA and IMD20 datasets to verify the effectiveness of our method, and the results demonstrate that the proposed method outperforms state-of-the-art methods by a wide margin based on on commonly used evaluation metrics.

External IDs:doi:10.1109/lsp.2024.3455230