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Nowadays, multimedia forensics faces unprecedented challenges due to therapid advancement of multimedia generation technology thereby making ImageManipulation Localization (IML) crucial in the pursuit of truth. The key to IMLlies in revealing the artifacts or inconsistencies between the tampered andauthentic areas, which are evident under pixel-level features. Consequently,existing studies treat IML as a low-level vision task, focusing on allocatingtampered masks by crafting pixel-level features such as image RGB noises, edgesignals, or high-frequency features. However, in practice, tampering commonlyoccurs at the object level, and different classes of objects have varyinglikelihoods of becoming targets of tampering. Therefore, object semantics arealso vital in identifying the tampered areas in addition to pixel-levelfeatures. This necessitates IML models to carry out a semantic understanding ofthe entire image. In this paper, we reformulate the IML task as a high-levelvision task that greatly benefits from low-level features. Based on such aninterpretation, we propose a method to enhance the Masked Autoencoder (MAE) byincorporating high-resolution inputs and a perceptual loss supervision module,which is termed Perceptual MAE (PMAE). While MAE has demonstrated an impressiveunderstanding of object semantics, PMAE can also compensate for low-levelsemantics with our proposed enhancements. Evidenced by extensive experiments,this paradigm effectively unites the low-level and high-level features of theIML task and outperforms state-of-the-art tampering localization methods on allfive publicly available datasets.

Perceptual MAE for Image Manipulation Localization: A High-level Vision Learner Focusing on Low-level Features