Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization

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 the 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 proposed 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. Furthermore, to mitigate the interference from general semantic object information, we propose the MNM that leverages multiple noise extractors based on the mixture of experts concept. This allows the MNM to learn semantic-agnostic forgery features from general RGB features, further boosting the performance of our proposed framework. Finally, we establish a comprehensive benchmark for multi-face detection and localization, and the proposed MoNFAP achieves significant performance. The code is available: https://github.com/miaoct/MoNFAP.