Powerful manipulation techniques have made digital image forgeries be easily
created and widespread without leaving visual anomalies. The blind localization
of tampered regions becomes quite significant for image forensics. In this
paper, we propose an effective image tampering localization network (EITLNet)
based on a two-branch enhanced transformer encoder with attention-based feature
fusion. Specifically, a feature enhancement module is designed to enhance the
feature representation ability of the transformer encoder. The features
extracted from RGB and noise streams are fused effectively by the coordinate
attention-based fusion module at multiple scales. Extensive experimental
results verify that the proposed scheme achieves the state-of-the-art
generalization ability and robustness in various benchmark datasets. Code will
be public at this https URL

本文提出了一种基于增强型双分支变压器编码器和基于注意力的特征融合的有效图像篡改定位网络 (EITLNet)，通过特征增强模块增强变压器编码器的特征表示能力，并且通过坐标注意力融合模块在多个尺度上有效地融合从 RGB 和噪声流提取的特征。广泛的实验结果验证了该方案在各种基准数据集上达到了最先进的泛化能力和鲁棒性。代码将在此 URL 进行公开。