  With the advancement of face manipulation technology, forgery images in
multi-face scenarios are gradually becoming a more complex and realistic
challenge. Despite this, detection and localization methods for such multi-face
manipulations remain underdeveloped. Traditional manipulation localization
methods either indirectly derive detection results from localization masks,
resulting in limited detection performance, or employ a naive two-branch
structure to simultaneously obtain detection and localization results, which
cannot effectively benefit the localization capability due to limited
interaction between two tasks. This paper proposes a new framework, namely
MoNFAP, specifically tailored for multi-face manipulation detection and
localization. The MoNFAP primarily introduces two novel modules: the
Forgery-aware Unified Predictor (FUP) Module and the Mixture-of-Noises Module
(MNM). The FUP integrates detection and localization tasks using a token
learning strategy and multiple forgery-aware transformers, which facilitates
the use of classification information to enhance localization capability.
Besides, motivated by the crucial role of noise information in forgery
detection, the MNM leverages multiple noise extractors based on the concept of
the mixture of experts to enhance the general RGB features, further boosting
the performance of our framework. Finally, we establish a comprehensive
benchmark for multi-face detection and localization and the proposed
\textit{MoNFAP} achieves significant performance. The codes will be made
available.
