MLPN: Multi-Scale Laplacian Pyramid Network for deepfake detection and localization

Yibo Zhang, Weiguo Lin, Junfeng Xu, Wanshang Xu, Yikun Xu

Abstract: Sophisticated and realistic facial manipulation videos created by deepfake technology have become ubiquitous, leading to profound trust crises and security risks in contemporary society. However, various researchers concentrate on enhancing the precision and generalization of deepfake detection models, with little attention to forgery localization. Detecting deepfakes and identifying fake regions is a challenging task. We propose an end-to-end model for performing deepfake detection and forgery localization based on the Laplacian pyramid. The model is designed by an encoder–decoder architecture. Specifically, the encoder generates multi-scale features. The decoder gradually integrates multi-scale features and Laplacian residuals to reconstruct the prediction masks coarse-to-finely. Otherwise, we adopt a spatial pyramid pool approach to deal with high-level semantic features and integrate local and global information. Comprehensive experiments demonstrate that the proposed model performs satisfactorily in deepfake detection and localization.