  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.
