  As deepfake content proliferates online, advancing face manipulation
forensics has become crucial. To combat this emerging threat, previous methods
mainly focus on studying how to distinguish authentic and manipulated face
images. Although impressive, image-level classification lacks explainability
and is limited to specific application scenarios, spurring recent research on
pixel-level prediction for face manipulation forensics. However, existing
forgery localization methods suffer from exploring frequency-based forgery
traces in the localization network. In this paper, we observe that
multi-frequency spectrum information is effective for identifying tampered
regions. To this end, a novel Multi-Spectral Class Center Network (MSCCNet) is
proposed for face manipulation detection and localization. Specifically, we
design a Multi-Spectral Class Center (MSCC) module to learn more generalizable
and multi-frequency features. Based on the features of different frequency
bands, the MSCC module collects multi-spectral class centers and computes
pixel-to-class relations. Applying multi-spectral class-level representations
suppresses the semantic information of the visual concepts which is insensitive
to manipulated regions of forgery images. Furthermore, we propose a Multi-level
Features Aggregation (MFA) module to employ more low-level forgery artifacts
and structural textures. Meanwhile, we conduct a comprehensive localization
benchmark based on pixel-level FF++ and Dolos datasets. Experimental results
quantitatively and qualitatively demonstrate the effectiveness and superiority
of the proposed MSCCNet. We expect this work to inspire more studies on
pixel-level face manipulation localization. The codes are available
(https://github.com/miaoct/MSCCNet).
