Recent studies have shown that the performance of forgery detection can be
improved with diverse and challenging Deepfakes datasets. However, due to the
lack of Deepfakes datasets with large variance in appearance, which can be
hardly produced by recent identity swapping methods, the detection algorithm
may fail in this situation. In this work, we provide a new identity swapping
algorithm with large differences in appearance for face forgery detection. The
appearance gaps mainly arise from the large discrepancies in illuminations and
skin colors that widely exist in real-world scenarios. However, due to the
difficulties of modeling the complex appearance mapping, it is challenging to
transfer fine-grained appearances adaptively while preserving identity traits.
This paper formulates appearance mapping as an optimal transport problem and
proposes an Appearance Optimal Transport model (AOT) to formulate it in both
latent and pixel space. Specifically, a relighting generator is designed to
simulate the optimal transport plan. It is solved via minimizing the
Wasserstein distance of the learned features in the latent space, enabling
better performance and less computation than conventional optimization. To
further refine the solution of the optimal transport plan, we develop a
segmentation game to minimize the Wasserstein distance in the pixel space. A
discriminator is introduced to distinguish the fake parts from a mix of real
and fake image patches. Extensive experiments reveal that the superiority of
our method when compared with state-of-the-art methods and the ability of our
generated data to improve the performance of face forgery detection.

本文提出了一种能够改善伪造检测性能的新的身份交换算法，将外观映射问题规范化为最优传输问题，并提出了一种外貌最优传输模型（AOT）以在潜在空间和像素空间中进行规范化。通过模拟最佳传输计划并通过在潜在空间中学习的特征的 Wasserstein 距离来解决它，使得性能更好，计算量比传统优化方法少。我们进一步通过引入鉴别器来将假的部分与真实和假的图像拼贴块的混合物区分开来，以进一步精细解决这个最优传输计划的问题，并证明了我们的方法相对于最先进的方法的优越性以及生成的数据改善面部伪造检测性能的能力