SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection

Since image editing methods in real world scenarios cannot be exhausted, generalization is a core challenge for image manipulation detection, which could be severely weakened by semantically related features. In this paper we propose SAFL-Net, which constrains a feature extractor to learn semantic-agnostic features by designing specific modules with corresponding auxiliary tasks. Applying constraints directly to the features extracted by the encoder helps it learn semantic-agnostic manipulation trace features, which prevents the biases related to semantic information within the limited training data and improves generalization capabilities. The consistency of auxiliary boundary prediction task and original region prediction task is guaranteed by a feature transformation structure. Experiments on various public datasets and comparisons in multiple dimensions demonstrate that SAFL-Net is effective for image manipulation detection.