Face Forgery Detection (FFD) plays a pivotal role in preserving privacy and bolstering information security by identifying counterfeit face images sourced from the internet. However, FFD encounters a significant challenge in terms of its limited capacity to generalize across diverse datasets due to the striking similarities between genuine and forged images. To tackle this issue, this paper introduces a novel approach known as Multi-level Distributional Discrepancy Enhancement (MDDE). The primary objective of MDDE is to discern variations in the distribution patterns of real and fake data at multiple levels of latent representations. To further enhance its capabilities for generalization, we incorporate a deformable convolution module that extracts intricate features from genuine images. The integration of this module equips MDDE with the ability to generalize to a broader range of samples. Extensive experiments conducted on extensive datasets verify the efficacy of our proposed method and its superior performance compared to several state-of-the-art techniques.