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.
@article{arxiv.2401.00653,
title = {PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning},
author = {Xuntao Liu and Yuzhou Yang and Qichao Ying and Zhenxing Qian and Xinpeng Zhang and Sheng Li},
journal= {arXiv preprint arXiv:2401.00653},
year = {2024}
}