Generic Image Manipulation Localization through the Lens of Multi-scale Spatial Inconsistence
Zan Gao, Shenghao Chen, Yangyang Guo, Weili Guan, Jie Nie, An-An Liu
Abstract
Image manipulation localization is of vital importance to public order protection. One dominant approach is to detect the anomalies in images, i.e., visual artifacts, as the tampered edge clue for aiding manipulation prediction. Nevertheless, we argue that these methods struggle with the modeling of spatial inconsistency within multi-scale, resulting in sub-optimal model performance. To overcome this problem, in this paper, we propose a novel end-to-end method to identify the multi-scale spatial inconsistency for image manipulation localization (abbreviated as MSI) where the multi-scale edge-guided attention stream (MEA) and multi-scale context-aware search stream (MCS) are jointly explored in a unified framework, moreover, multi-scale information is efficiently used. In the former, the edge-attention module is designed to precisely locate the tampered regions based upon multi-scale edge boundary features. In the latter, the context-aware search module is designed to model spatial contextual information within multiple scales. To validate the effectiveness of the proposed method, we conduct extensive experiments on six image manipulation localization datasets including NIST-2016, Columbia, CASIA1.0, COVER, DEF-12K, and IMD2020. The experimental results demonstrate that our proposed method can outperform state-of-the-art methods by a significant margin in terms of average F1 score while maintaining robustness with respect to various attacks. Compared with MVSS-Net (Published in ICCV 2021) on the NIST-2016, CASIA1.0, DEF-12K, and IMD2020 datasets, the improvements in F1 score can reach 6.7%, 9.5%, 5.4%, and 8.4%, respectively.