The rapid development of deepfake generation techniques necessitates robust face forgery detection algorithms. While methods based on Convolutional Neural Networks (CNNs) and Transformers are effective, there is still room for improvement in modeling the highly complex and non-linear nature of forgery artifacts. To address this issue, we propose a novel detection method based on the Kolmogorov-Arnold Network (KAN). By replacing fixed activation functions with learnable splines, our KAN-based approach is better suited to this challenge. Furthermore, to guide the network's focus towards critical facial areas, we introduce a Landmark-assisted Adaptive Kolmogorov-Arnold Network (LAKAN) module. This module uses facial landmarks as a structural prior to dynamically generate the internal parameters of the KAN, creating an instance-specific signal that steers a general-purpose image encoder towards the most informative facial regions with artifacts. This core innovation creates a powerful combination between geometric priors and the network's learning process. Extensive experiments on multiple public datasets show that our proposed method achieves superior performance.

翻译：深度伪造生成技术的快速发展对鲁棒的人脸伪造检测算法提出了迫切需求。尽管基于卷积神经网络（CNN）和Transformer的方法已取得一定效果，但在建模高度复杂且非线性的伪造伪影方面仍有改进空间。为解决这一问题，我们提出了一种基于Kolmogorov-Arnold网络（KAN）的新型检测方法。通过将固定激活函数替换为可学习的样条函数，我们基于KAN的方法能更好地应对这一挑战。此外，为引导网络聚焦于关键的面部区域，我们引入了基于面部关键点辅助的自适应Kolmogorov-Arnold网络（LAKAN）模块。该模块利用面部关键点作为结构先验，动态生成KAN的内部参数，从而创建实例特定的信号，引导通用图像编码器关注包含伪影的最具信息量的面部区域。这一核心创新实现了几何先验与网络学习过程的强大结合。在多个公开数据集上的大量实验表明，我们提出的方法取得了卓越的性能。