With the rapid development of deep generative models， the realism of synthetic images has been continuously improving， and various generative technologies have been deeply integrated into people’s daily life， from image generation to face manipulation， which brings attention to the authenticity of images. In addition， mainstream image classification models are mainly pre-trained on natural scene datasets with rich and varied styles， while a single prompt can generate a large amount of data， but there is an obvious homogeneity problem， which affects the imbalance of learning difficulty， thus making the traditional image binary classification training method in the generated image detection task have insufficient generalization ability. To address such issues， we propose a detection method under the difficulty and easy sample imbalance， which does not need to modify the existing classification model， and establishes an effective data augmentation paradigm by generating data self-enhancement to expand the diversity of generated data， thereby balancing the learning difficulty of the model. At the same time， we use the corrected class cross-entropy loss for sensitive punishment in difficult and easy samples. Finally， the proposed method achieves the best results in the computer vision application challenge： Real and fake image recognition competition held by the artificial intelligence society of shandong province in November 2023．