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

PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning