Wavelet Convolution and Multi-Scale Attention Network for Image Tampering Localization

Yun Song, Yaoyao Xu, Jiaxin Chen, He Yang, Dengyong Zhang, Miaohui Wang

Abstract: Conventional tampering localization only extracts features in the image domain, which makes it hard to capture the subtle tampering traces. In this paper, we propose a wavelet convolution and multi-scale attention network (WCMA-Net) for image tampering detection and localization, in which a wavelet convolution module (WCM) branch and a multi-scale attention module (MSAM) branch are integrated following the backbone. In the WCM branch, wavelet decomposition is utilized to enhance high-frequency details and enhance the detection of subtle tampering traces. In the MSAM branch, a multi-scale attention operation is employed to extract global and local features, which are then combined according to their similarity to capture long-range dependencies among pixels. Finally, an adaptive weight strategy is employed to fuse the features from both branches for binary pixel-level tampering mask prediction. Experimental results on various public datasets demonstrate that the proposed method achieves superior precise pixel-level image tampering localization over state-of-the-art methods. Codes and models are available at https://github.com/csust-sonie/WCMA-Net.

External IDs:dblp:conf/icmcs/SongXCYZW25