Abstract
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.
Abstract (translated)
深度伪造生成技术的迅速发展要求有强大的面部伪造检测算法。虽然基于卷积神经网络（CNN）和变换器的方法已经很有效，但在建模高度复杂且非线性的伪造痕迹方面仍有改进空间。为了解决这个问题，我们提出了一种基于柯尔莫哥洛夫-阿诺德网络（KAN）的新型检测方法。通过用可学习样条替换固定的激活函数，我们的基于KAN的方法更适用于这一挑战。此外，为了引导网络聚焦于关键面部区域，我们引入了一个名为“Landmark-assisted Adaptive Kolmogorov-Arnold Network”（LAKAN）模块。该模块使用面部标志作为结构先验，动态生成KAN的内部参数，从而创建一个特定实例信号，将通用图像编码器引导至最具信息量且包含伪造痕迹的脸部区域。这一核心创新在几何先验和网络学习过程之间创造了一种强大的结合方式。在多个公开数据集上的广泛实验表明，我们提出的方法取得了卓越性能。
URL
https://arxiv.org/abs/2510.00634
