With the rapid advances of image editing techniques in recent years, image manipulation detection has attracted considerable attention since the increasing security risks posed by tampered images. To address these challenges, a novel multi-scale multi-grained deep network (MSMG-Net) is proposed to automatically identify manipulated regions. In our MSMG-Net, a parallel multi-scale feature extraction structure is used to extract multi-scale features. Then the multi-grained feature learning is utilized to perceive object-level semantics relation of multi-scale features by introducing the shunted self-attention. To fuse multi-scale multi-grained features, global and local feature fusion block are designed for manipulated region segmentation by a bottom-up approach and multi-level feature aggregation block is designed for edge artifacts detection by a top-down approach. Thus, MSMG-Net can effectively perceive the object-level semantics and encode the edge artifact. Experimental results on five benchmark datasets justify the superior performance of the proposed method, outperforming state-of-the-art manipulation detection and localization methods. Extensive ablation experiments and feature visualization demonstrate the multi-scale multi-grained learning can present effective visual representations of manipulated regions. In addition, MSMG-Net shows better robustness when various post-processing methods further manipulate images.
翻译:随着近年来图像编辑技术的迅速发展,图像操纵探测吸引了相当多的关注,因为被篡改的图像所构成的安全风险日益增大。为了应对这些挑战,建议建立一个新的多规模多层深层网络(MSMG-Net),以自动识别被操纵的区域。在我们的MSMG-Net中,使用一个平行的多尺度特征提取结构来提取多尺度特征。然后,多级特征学习被利用,通过引入被筛选的自我意识来了解多尺度特征的物体级语义关系。为了结合多规模多级多级多级图像,设计了一个全球和本地地貌融合区块,通过自下而上而上而上的方式用于操纵的区域分割。因此,MSMG-Net可以有效地看到物体级语义和边端文物的编码。五个基准数据集的实验结果证明拟议方法的优异性性,优异于状态的操纵检测和本地化方法。在MSMG的多级化后演化中,可以进一步进行宽广范围的图像化实验和特征视觉化实验,展示了当前多级的多级图像处理方法。