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

Understand

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.