  Deceptive images can be shared in seconds with social networking services,
posing substantial risks. Tampering traces, such as boundary artifacts and
high-frequency information, have been significantly emphasized by massive
networks in the Image Manipulation Localization (IML) field. However, they are
prone to image post-processing operations, which limit the generalization and
robustness of existing methods. We present a novel Prompt-IML framework. We
observe that humans tend to discern the authenticity of an image based on both
semantic and high-frequency information, inspired by which, the proposed
framework leverages rich semantic knowledge from pre-trained visual foundation
models to assist IML. We are the first to design a framework that utilizes
visual foundation models specially for the IML task. Moreover, we design a
Feature Alignment and Fusion module to align and fuse features of semantic
features with high-frequency features, which aims at locating tampered regions
from multiple perspectives. Experimental results demonstrate that our model can
achieve better performance on eight typical fake image datasets and outstanding
robustness.
