Deepfake detection methods based on convolutional neural networks (CNN) have demonstrated high accuracy. \textcolor{black}{However, these methods often suffer from decreased performance when faced with unknown forgery methods and common transformations such as resizing and blurring, resulting in deviations between training and testing domains.} This phenomenon, known as overfitting, poses a significant challenge. To address this issue, we propose a novel block shuffling regularization method. Firstly, our approach involves dividing the images into blocks and applying both intra-block and inter-block shuffling techniques. This process indirectly achieves weight-sharing across different dimensions. Secondly, we introduce an adversarial loss algorithm to mitigate the overfitting problem induced by the shuffling noise. Finally, we restore the spatial layout of the blocks to capture the semantic associations among them. Extensive experiments validate the effectiveness of our proposed method, which surpasses existing approaches in forgery face detection. Notably, our method exhibits excellent generalization capabilities, demonstrating robustness against cross-dataset evaluations and common image transformations. Especially our method can be easily integrated with various CNN models. Source code is available at \href{https://github.com/NoWindButRain/BlockShuffleLearning}{Github}.

翻译：基于卷积神经网络（CNN）的深度伪造检测方法已展现出高精度。然而，当面对未知伪造方法以及缩放和模糊等常见变换时，这些方法往往性能下降，导致训练域与测试域之间出现偏差。这种被称为过拟合的现象构成了重大挑战。为解决此问题，我们提出一种新颖的区块洗牌正则化方法。首先，我们的方法将图像划分为区块，并对这些区块实施块内与块间洗牌技术，从而间接实现跨维度的权重共享。其次，我们引入一种对抗损失算法以减轻由洗牌噪声引发的过拟合问题。最后，我们恢复区块的空间布局，以捕捉它们之间的语义关联。大量实验验证了我们提出方法的有效性，其在伪造人脸检测方面超越了现有方法。值得注意的是，我们的方法展现出卓越的泛化能力，对跨数据集评估及常见图像变换具有鲁棒性。尤其是，该方法易于与各类CNN模型集成。源代码可在 \href{https://github.com/NoWindButRain/BlockShuffleLearning}{Github} 上获取。