Deep learning models face some security risks, and security breaches in their applications can lead to severe consequences. Enhancing the security of deep learning models is therefore necessary. Existing reinforcement methods include adversarial training against adversarial noise, adversarial noise purification methods, and others. Among them, adversarial training is the most widely used method and provides effective defense against adversarial attacks. However, models trained with adversarial training often suffer from insufficient robust generalization and considerable loss of original accuracy. To address these issues, this study proposes a new adversarial training method called proxy-distribution-based adversarial training (PBAT). The proposed method employs a probabilistic model to capture the distribution patterns of data samples and generate enhanced training samples that balance original accuracy and robustness. The resilience of the model is further enhanced through an adjusted training process. Experiments are conducted using ResNet-20 and GoogLeNet on two benchmark datasets, CIFAR10 and MNIST. Furthermore, experiments are conducted on the Faster R-CNN model and the PASCAL VOC dataset for object detection tasks. The experimental results demonstrate that PBAT outperforms four representative methods.