MemAU-Net: Memory-Enhanced Attention U-Net for Medical Image Forgery Localization

Nan Wang, Liping Yi, Gang Wang, Xiaoguang Liu

Abstract: Medical image forgery has become an urgent issue in academia and medicine. Unlike natural images, images in the medical field are so sensitive that even minor manipulation can produce severe consequences. In view of the specificities of medical images, natural image forgery localization methods are difficult to generalize. While the unsatisfactory performance of existing medical image forgery localization methods, we propose MemAU-Net: a copy-move and splicing resistant medical image forgery localization network. We propose a novel attention gate named Memory-Enhanced Attention Gate (MAG), which effectively fuses shallow-deep features and improves feature representation to make attention better suit for medical image forgery localization tasks. To minimize holes and edge serrations caused by the delicate and blurred textures in medical images, we use dense CRF to smooth the boundaries, reduce false alarms and missed detection. Since there is no medical image forgery dataset publicly available, by using the copy-move and splicing forgery operations, we manually tamper and annotate two forged medical image datasets: OIAT (eye) and COVIDLT (lung) to verify the generality of the proposed model. The dataset includes the images from funds and different views of the lungs, corresponding to the specificity of medical images with flat grayscale changes and complex textures. The results show that by using MemAU-Net alone, we can improve the F-measure by 2.33% and 4.61% over the state-of-the-art baseline on these two datasets, respectively. Moreover, the raised precisions and enhanced visualizations with the addition of dense CRF indicate that it effectively removes false positives, fills holes and smooths edges. A good proof of the superiority of MemAU-Net's medical image forgery localization function is provided by these results. In addition, the proposed method achieves promising robustness to forgery post-processing such as rotation, scaling and anti-forensic attacks like noise.