Generative adversarial networks (GANs) have remarkably advanced in diverse
domains, especially image generation and editing. However, the misuse of GANs
for generating deceptive images raises significant security concerns, including
face replacement and fake accounts, which have gained widespread attention.
Consequently, there is an urgent need for effective detection methods to
distinguish between real and fake images. Some of the 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 the above issues, this paper introduces a novel GAN-generated image
detection algorithm called X-Transfer. This model enhances transfer learning by
utilizing two sibling neural networks that employ interleaved parallel gradient
transmission. This approach also effectively mitigates the problem of excessive
knowledge forgetting. In addition, we combine AUC loss term and cross-entropy
loss to enhance the model's performance comprehensively. The AUC loss
approximates the AUC metric using WMW statistics, ensuring differentiability
and improving the performance of traditional AUC evaluation. We carry out
comprehensive experiments on multiple facial image datasets. The results show
that our model outperforms the general transferring approach, and the best
accuracy 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.

本文介绍了一种名为 X-Transfer 的 GAN 生成图像检测算法，该模型通过使用两个采用交替并行梯度传输的兄弟神经网络来增强迁移学习，并有效缓解了过度知识遗忘的问题。此外，我们结合了 AUC 损失项和交叉熵损失，全面提升了模型的性能。通过在多个面部图像数据集上进行全面实验，结果表明我们的模型优于一般的迁移方法，最高准确率达到了 99.04%，提升了约 10%。此外，我们在非面部数据集上展示了出色的性能，验证了其广泛应用前景。