用人脸关键点引导网络，精准识别深度伪造图像。
LAKAN: Landmark-assisted Adaptive Kolmogorov-Arnold Network for Face Forgery Detection
- 用可学习样条替代固定激活函数，更好捕捉伪造痕迹的非线性特征。
- 通过关键点动态调整网络参数，在多个数据集上检测准确率领先。
- 适合需要高精度伪造检测的安防、媒体审核场景。
深度伪造技术的快速发展亟需强大的面部伪造检测算法。尽管基于卷积神经网络（CNN）和变压器（Transformer）的方法有效，但在建模伪造痕迹高度复杂且非线性的特性方面仍有提升空间。为此，我们提出一种基于柯尔莫哥洛夫-阿诺德网络（KAN）的新检测方法。通过用可学习样条替换固定激活函数，该方法更适应此挑战。此外，为引导网络关注关键面部区域，我们引入了基于关键点的自适应柯尔莫哥洛夫-阿诺德网络（LAKAN）模块。该模块利用人脸关键点作为结构先验，动态生成KAN内部参数，形成实例特定信号，引导通用图像编码器聚焦最具信息量的伪造区域。这一核心创新实现了几何先验与网络学习过程的深度融合。在多个公开数据集上的大量实验表明，所提方法性能卓越。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。