In recent years, the realism of generated face images has significantly improved, making it increasingly difficult for the public to distinguish between real and fake. Existing detection methods often struggle to capture local artifacts in these generated images, presenting limitations when confronted with highly realistic synthetic faces. To address this, this paper proposes a completely new evaluation metrics, the Local Artifact Metrics (LAM), which quantifies the degree of artifacts in key regions of generated face images, providing a more accurate assessment of overall image authenticity. In addition, to enhance the ability to capture generation artifacts, we introduce gradient maps into the Convolutional Block Attention Module (CBAM), resulting in a modified attention mechanism referred to as the Gradient-Enhanced Attention Mechanism (GEAM). Experimental results demonstrate that the proposed generated faces detection method achieves superior detection performance across multiple fake face datasets.