Recursive wavelet transform network for robust copy-move forgery detection

In RWTN-Net, the Frequency Rotational-Invariant Feature Extractor (FRFE) firstly performs multi-stage wavelet transform and Sorted Convolution to extract multi-scale rotational invariant low-frequency and high-frequency features, which are robust to geometric transformations. Then, an Adaptive Multi-Scale Attention Fusion (AMAF) is designed to fuse features of different scales with an adaptive attention. The channel weights of low-resolution features are used to guide the weights allocation of high-resolution features, thereby enhancing the network’s understanding of geometric details and semantic information. Moreover, the Local Average Self-Correlation Calculation (LASCC) adopts a diagonal-guided sparse sampling strategy to select key feature points along the diagonal of each patch in the feature map for correlation calculation, which effectively improves the computational efficiency. Finally, a localization module is deployed to combine the matching maps of different receptive fields in an accumulative manner, and an adaptive U-net is further employed to obtain accurate localization results. Experimental results on public datasets demonstrate the effectiveness of the proposed RWTN-Net. The source code is available at https://github.com/studyimg/RWTN-Net.