Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing. However, the misuse of GANs for generating deceptive images, such as face replacement, raises significant security concerns, which have gained widespread attention. Therefore, it is urgent to develop effective detection methods to distinguish between real and fake images. Current research centers around the application of transfer learning. Nevertheless, it encounters challenges such as knowledge forgetting from the original dataset and inadequate performance when dealing with imbalanced data during training. To alleviate this issue, this paper introduces a novel GAN-generated image detection algorithm called X-Transfer, which enhances transfer learning by utilizing two neural networks that employ interleaved parallel gradient transmission. In addition, we combine AUC loss and cross-entropy loss to improve the model's performance. We carry out comprehensive experiments on multiple facial image datasets. The results show that our model outperforms the general transferring approach, and the best metric achieves 99.04%, which is increased by approximately 10%. Furthermore, we demonstrate excellent performance on non-face datasets, validating its generality and broader application prospects.

翻译：生成对抗网络（GANs）在图像生成与编辑等多个领域取得了显著进展。然而，GANs被滥用于生成具有欺骗性的图像（例如换脸）引发了重大的安全隐患，这一问题已受到广泛关注。因此，亟需开发有效的检测方法来区分真实图像与伪造图像。当前研究主要围绕迁移学习的应用展开，但仍面临诸多挑战，包括对原始数据集的知识遗忘问题，以及在训练过程中处理不平衡数据时表现欠佳等。为缓解上述问题，本文提出一种名为X-Transfer的新型GAN生成图像检测算法，该算法通过采用两个以交错并行梯度传输方式运作的神经网络来增强迁移学习。此外，我们融合AUC损失与交叉熵损失以提升模型性能。我们在多个人脸图像数据集上开展了全面实验，结果表明：本模型优于通用迁移方法，最佳指标达到99.04%，提升了约10%。同时，我们还在非人脸数据集上验证了其优异表现，证实了该方法的通用性与更广阔的应用前景。