  Why should we trust the detections of deep neural networks for manipulated
faces? Understanding the reasons is important for users in improving the
fairness, reliability, privacy and trust of the detection models. In this work,
we propose an interpretable face manipulation detection approach to achieve the
trustworthy and accurate inference. The approach could make the face
manipulation detection process transparent by embedding the feature whitening
module. This module aims to whiten the internal working mechanism of deep
networks through feature decorrelation and feature constraint. The experimental
results demonstrate that our proposed approach can strike a balance between the
detection accuracy and the model interpretability.
